429 results on '"Bartolucci, F."'
Search Results
2. Modelling the long-term health impact of COVID-19 using Graphical Chain Models brief heading: long COVID prediction by graphical chain models.
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Gourgoura, K., Rivadeneyra, P., Stanghellini, E., Caroni, C., Bartolucci, F., Curcio, R., Bartoli, S., Ferranti, R., Folletti, I., Cavallo, M., Sanesi, L., Dominioni, I., Santoni, E., Morgana, G., Pasticci, M. B., Pucci, G., and Vaudo, G.
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POST-acute COVID-19 syndrome ,COVID-19 ,SYMPTOMS ,PULMONARY function tests ,EXERCISE tests - Abstract
Background: Long-term sequelae of SARS-CoV-2 infection, namely long COVID syndrome, affect about 10% of severe COVID-19 survivors. This condition includes several physical symptoms and objective measures of organ dysfunction resulting from a complex interaction between individual predisposing factors and the acute manifestation of disease. We aimed at describing the complexity of the relationship between long COVID symptoms and their predictors in a population of survivors of hospitalization for severe COVID-19-related pneumonia using a Graphical Chain Model (GCM). Methods: 96 patients with severe COVID-19 hospitalized in a non-intensive ward at the "Santa Maria" University Hospital, Terni, Italy, were followed up at 3–6 months. Data regarding present and previous clinical status, drug treatment, findings recorded during the in-hospital phase, presence of symptoms and signs of organ damage at follow-up were collected. Static and dynamic cardiac and respiratory parameters were evaluated by resting pulmonary function test, echocardiography, high-resolution chest tomography (HRCT) and cardiopulmonary exercise testing (CPET). Results: Twelve clinically most relevant factors were identified and partitioned into four ordered blocks in the GCM: block 1 - gender, smoking, age and body mass index (BMI); block 2 - admission to the intensive care unit (ICU) and length of follow-up in days; block 3 - peak oxygen consumption (VO
2 ), forced expiratory volume at first second (FEV1 ), D-dimer levels, depression score and presence of fatigue; block 4 - HRCT pathological findings. Higher BMI and smoking had a significant impact on the probability of a patient's admission to ICU. VO2 showed dependency on length of follow-up. FEV1 was related to the self-assessed indicator of fatigue, and, in turn, fatigue was significantly associated with the depression score. Notably, neither fatigue nor depression depended on variables in block 2, including length of follow-up. Conclusions: The biological plausibility of the relationships between variables demonstrated by the GCM validates the efficacy of this approach as a valuable statistical tool for elucidating structural features, such as conditional dependencies and associations. This promising method holds potential for exploring the long-term health repercussions of COVID-19 by identifying predictive factors and establishing suitable therapeutic strategies. [ABSTRACT FROM AUTHOR]- Published
- 2024
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3. Lectotypification and taxonomy of the Italian endemic <italic>Biscutella incana</italic> (Brassicaceae)
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Bartolucci, F. and Conti, F.
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BOTANY , *BRASSICACEAE , *PHENOLOGY , *CHROMOSOMES , *TAXONOMY - Abstract
Abstract
Biscutella incana is endemic to the southern Apennine Peninsula (Italy) and was first described by Michele Tenore in 1826 from Calabria. It belongs toB. ser.Levigatae , the most morphologically diversified and critical series within the genus. In order to fix the application of this name, a lectotype housed in NAP was designated here. An updated and detailed morphological description, distribution and information about habitat and phenology are provided. Furthermore, a chromosome count made on a new population discovered in Dolomiti Lucane (Basilicata, southern Italy), confirmed thatB. incana is diploid (2n = 18). [ABSTRACT FROM AUTHOR]- Published
- 2024
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4. Modelling Nonstationary Spatial Lag Models with Hidden Markov Random Fields
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Ghiringhelli, C., Bartolucci, F., Mira, A., and Arbia, G.
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- 2021
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5. A second update to the checklist of the vascular flora native to Italy.
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Bartolucci, F., Peruzzi, L., Galasso, G., Alessandrini, A., Ardenghi, N. M. G., Bacchetta, G., Banfi, E., Barberis, G., Bernardo, L., Bouvet, D., Bovio, M., Calvia, G., Castello, M., Cecchi, L., Del Guacchio, E., Domina, G., Fascetti, S., Gallo, L., Gottschlich, G., and Guarino, R.
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BOTANY , *FERNS , *PTERIDOPHYTA , *SUBSPECIES , *PLANT diversity , *LYCOPHYTES - Abstract
Critical species inventories provide primary biodiversity data crucial for biogeographical, ecological, and conservation studies. After six years, a second update to the inventory of the vascular flora native to Italy is presented. It provides details on the occurrence at regional level and, for the first time, floristic data for San Marino. The checklist includes 8,241 species and subspecies, distributed in 1,111 genera and 153 families; 23 taxa are lycophytes, 108 ferns and fern allies, 30 gymnosperms, and 8,080 angiosperms. The species/subspecies endemic to Italy are 1,702, grouped in 71 families and 312 genera. The taxa currently occurring in Italy are 7,591, while 545 taxa have not been confirmed in recent times, 94 are doubtfully occurring in the country, 11 are data deficient, and 236 are reported by mistake and to be excluded at national level. Out of the 545 not confirmed taxa, 28 are considered extinct or possibly extinct. [ABSTRACT FROM AUTHOR]
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- 2024
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6. IMPLEMENTATION OF THE IAEA TRS-483 FIELD OUTPUT CORRECTION FACTORS: DOSIMETRIC IMPACT ON CLINICAL STEREOTACTIC VMAT PLANS
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Savini, A., primary, Bartolucci, F., additional, Fidanza, C., additional, and Rosica, F., additional
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- 2023
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7. Causal inference in paired two-arm experimental studies under non-compliance with application to prognosis of myocardial infarction
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Bartolucci, F. and Farcomeni, A.
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Mathematics - Statistics Theory - Abstract
Motivated by a study about prompt coronary angiography in myocardial infarction, we propose a method to estimate the causal effect of a treatment in two-arm experimental studies with possible non-compliance in both treatment and control arms. The method is based on a causal model for repeated binary outcomes (before and after the treatment), which includes individual covariates and latent variables for the unobserved heterogeneity between subjects. Moreover, given the type of non-compliance, the model assumes the existence of three subpopulations of subjects: compliers, never-takers, and always-takers. The model is estimated by a two-step estimator: at the first step the probability that a subject belongs to one of the three subpopulations is estimated on the basis of the available covariates; at the second step the causal effects are estimated through a conditional logistic method, the implementation of which depends on the results from the first step. Standard errors for this estimator are computed on the basis of a sandwich formula. The application shows that prompt coronary angiography in patients with myocardial infarction may significantly decrease the risk of other events within the next two years, with a log-odds of about -2. Given that non-compliance is significant for patients being given the treatment because of high risk conditions, classical estimators fail to detect, or at least underestimate, this effect.
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- 2012
8. Nested hidden Markov chains for modeling dynamic unobserved heterogeneity in multilevel longitudinal data
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Bartolucci, F. and Lupparelli, M.
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Mathematics - Statistics Theory - Abstract
In the context of multilevel longitudinal data, where sample units are collected in clusters, an important aspect that should be accounted for is the unobserved heterogeneity between sample units and between clusters. For this aim we propose an approach based on nested hidden (latent) Markov chains, which are associated to every sample unit and to every cluster. The approach allows us to account for the mentioned forms of unobserved heterogeneity in a dynamic fashion; it also allows us to account for the correlation which may arise between the responses provided by the units belonging to the same cluster. Given the complexity in computing the manifest distribution of these response variables, we make inference on the proposed model through a composite likelihood function based on all the possible pairs of subjects within every cluster. The proposed approach is illustrated through an application to a dataset concerning a sample of Italian workers in which a binary response variable for the worker receiving an illness benefit was repeatedly observed.
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- 2012
9. A note on the application of the Oakes' identity to obtain the observed information matrix of hidden Markov models
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Bartolucci, F., Farcomeni, A., and Pennoni, F.
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Mathematics - Statistics Theory - Abstract
We derive the observed information matrix of hidden Markov models by the application of the Oakes (1999)'s identity. The method only requires the first derivative of the forward-backward recursions of Baum and Welch (1970), instead of the second derivative of the forward recursion, which is required within the approach of Lystig and Hughes (2002). The method is illustrated by an example based on the analysis of a longitudinal dataset which is well known in sociology.
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- 2012
10. A generalized Multiple-try Metropolis version of the Reversible Jump algorithm
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Pandolfi, S., Bartolucci, F., and Friel, N.
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Statistics - Methodology - Abstract
The Reversible Jump algorithm is one of the most widely used Markov chain Monte Carlo algorithms for Bayesian estimation and model selection. A generalized multiple-try version of this algorithm is proposed. The algorithm is based on drawing several proposals at each step and randomly choosing one of them on the basis of weights (selection probabilities) that may be arbitrary chosen. Among the possible choices, a method is employed which is based on selection probabilities depending on a quadratic approximation of the posterior distribution. Moreover, the implementation of the proposed algorithm for challenging model selection problems, in which the quadratic approximation is not feasible, is considered. The resulting algorithm leads to a gain in efficiency with respect to the Reversible Jump algorithm, and also in terms of computational effort. The performance of this approach is illustrated for real examples involving a logistic regression model and a latent class model.
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- 2010
11. An overview of latent Markov models for longitudinal categorical data
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Bartolucci, F., Farcomeni, A., and Pennoni, F.
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Mathematics - Statistics Theory - Abstract
We provide a comprehensive overview of latent Markov (LM) models for the analysis of longitudinal categorical data. The main assumption behind these models is that the response variables are conditionally independent given a latent process which follows a first-order Markov chain. We first illustrate the basic LM model in which the conditional distribution of each response variable given the corresponding latent variable and the initial and transition probabilities of the latent process are unconstrained. For this model we also illustrate in detail maximum likelihood estimation through the Expectation-Maximization algorithm, which may be efficiently implemented by recursions known in the hidden Markov literature. We then illustrate several constrained versions of the basic LM model, which make the model more parsimonious and allow us to include and test hypotheses of interest. These constraints may be put on the conditional distribution of the response variables given the latent process (measurement model) or on the distribution of the latent process (latent model). We also deal with extensions of LM model for the inclusion of individual covariates and to multilevel data. Covariates may affect the measurement or the latent model; we discuss the implications of these two different approaches according to the context of application. Finally, we outline methods for obtaining standard errors for the parameter estimates, for selecting the number of states and for path prediction. Models and related inference are illustrated by the description of relevant socio-economic applications available in the literature.
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- 2010
12. Analysis of Sacco Hospital longitudinal data by hidden Markov models
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Pennoni, F, Bartolucci, F, Spinelli, D, Vittadini, G, Pennoni, F, Bartolucci, F, Spinelli, D, and Vittadini, G
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SECS-S/01 - STATISTICA ,chance of recovering, Expectation-Maximization algorithm, multivariate binary longitudinal categorical responses, post-.covid symptoms - Published
- 2023
13. PC-01.9 - IMPLEMENTATION OF THE IAEA TRS-483 FIELD OUTPUT CORRECTION FACTORS: DOSIMETRIC IMPACT ON CLINICAL STEREOTACTIC VMAT PLANS
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Savini, A., Bartolucci, F., Fidanza, C., and Rosica, F.
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- 2023
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14. Conserving plant diversity in Europe: outcomes, criticisms and perspectives of the Habitats Directive application in Italy
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Fenu, G., Bacchetta, G., Giacanelli, V., Gargano, D., Montagnani, C., Orsenigo, S., Cogoni, D., Rossi, G., Conti, F., Santangelo, A., Pinna, M. S., Bartolucci, F., Domina, G., Oriolo, G., Blasi, C., Genovesi, P., Abeli, T., and Ercole, S.
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- 2017
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15. Tempered Expectation-Maximization algorithm for discrete latent variable models
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Brusa, L, Bartolucci, F, Pennoni, F, Yuichi Mori, Hiroshi Yadohisa, Tomokazu Fujino, Hidetoshi Murakami, Wataru Sakamoto, Fumitake Sakaori, Hirohito Sakurai, Yoshikazu Terada, Makoto Tomita, Kensuke Okada, Kosuke Okusa, Koji Yamamoto, Michio Yamamoto, Yoshiro Yamamoto, Yoshitomo Akimoto, Brusa, L, Bartolucci, F, and Pennoni, F
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SECS-S/01 - STATISTICA ,Annealing, Global maximum, Hidden Markov model, Latent class model, Local maxima - Abstract
The Latent Class (LC) model is one of the most well-known latent variable models; it is very popular for the analysis of categorical response variables, and it is typically used to cluster subjects, by assuming the existence of individual-specific latent variables having a discrete distribution. A Hidden (or Latent) Markov (HM) model represents a generalization of the LC model to the case of longitudinal data. It assumes the existence of a discrete latent process generally following a first-order Markov chain, corresponding to subpopulations, usually referred to as latent states. As typically happens for discrete latent variable models, despite maximum likelihood estimation of both LC and HM model parameters can be rather simply performed using the Expectation-Maximization (EM) algorithm, a well-known drawback of this estimation method is related to the multimodality of the log-likelihood function. The consequence is that the estimation algorithm could converge to one of the local maxima, not corresponding to the global optimum. In order to face the multimodality problem described above, we propose a Tempered EM (T-EM) algorithm, which is able to explore the parameter space adequately. It consists in rescaling the objective function depending on a parameter known as the temperature, which controls global and local maxima prominence. High temperatures allow us to explore wide regions of the parameter space, avoiding the maximization algorithm being trapped in non-global maxima; low temperatures, instead, guarantee a sharp optimization in a local region of the parameter space. By properly tuning the sequence of temperature values, the target function is gradually attracted towards the global maximum, escaping local sub-optimal solutions. We rely on an accurate Monte Carlo simulation study to compare the proposal with the standard EM algorithm, evaluating both the ability to hit the global maximum and the computational time of the proposed algorithm. We also show the results for both LC and HM models, using the proposal on discrete and continuous cross-sectional and longitudinal data in connection with some applications of interest. We conclude that the proposal outperforms the standard EM algorithm, significantly improving the chance to reach the global maximum in the overwhelming majority of considered cases. The advantage is relevant even considering the overall computing time.
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- 2022
16. A Regime switching Student-t copula model for the analysis of cryptocurrencies data
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Cortese, F., Bartolucci, F., Pennoni, F., Marco Corazza, Cira Perna, Claudio Pizzi, Marilena Sibillo, Magni, G, Bimonte, G, Naimoli, A, Cortese, F, Bartolucci, F, and Pennoni, F
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copula models, cryptocurrencies, daily log-returns, Expectation-Maximization algorithm, latent variable models ,SECS-S/01 - STATISTICA - Abstract
Flexible statistical models have an important role in explaining the joint distribution of financial returns. In these analyses, it is necessary to consider abrupt switches in the market conditions, especially if the focus is on cryptoassets, the market of which is characterized by high instabilities. Regime switching (RS) copula models represent a powerful tool to formulate the joint distribution of time-series accurately: they are based on a copula distribution with parameters governed by a hidden Markov process of first-order so as to account for the correlation patterns between series. The hidden states represent different market regimes, each described by a state-specific vector of copula parameters. We propose RS copula models as a valuable instrument for describing the joint behavior of log- returns. We choose a Student-t copula function to consider extreme dependent values appropriately as they are often observed in financial returns. We split the modeling process into two steps: the first one consists in fitting the marginal distribution of each univariate time-series, while the second one deals with the estimation of the joint distribution of the log-returns described by a RS copula model. Maximum likelihood estimation of the model parameters is carried out by the expectation-maximization (EM) algorithm, which alternates two steps until convergence: at the E-step, we compute the expectation of the log-likelihood evaluated using the current values for the parameters and, at the M-step, parameters estimates are updated by maximizing the expected complete-data log-likelihood computed at the previous step. The main computational burdens deal with estimating the correlation matrix (R) and the number of degrees of freedom (v) of the Student t-copula. At this aim, we propose performing the M-step by computing R given v using a closed form solution obtained from a constrained optimization of the log-likelihood using Lagrange multipliers. Then, we numerically maximize the log-likelihood with respect to v given the previous update of R. The proposal is validated through a simulation study showing that the estimators have good finite sample properties. We consider data on daily log-returns over four years of five cryptos Bitcoin, Bitcoin Cash, Ethereum, Litecoin, and Ripple as an application.
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- 2022
17. Dimensionality of the Latent Structure and Item Selection via Latent Class Multidimensional IRT Models
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Bartolucci, F., Montanari, G. E., and Pandolfi, S.
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With reference to a questionnaire aimed at assessing the performance of Italian nursing homes on the basis of the health conditions of their patients, we investigate two relevant issues: dimensionality of the latent structure and discriminating power of the items composing the questionnaire. The approach is based on a multidimensional item response theory model, which assumes a two-parameter logistic parameterization for the response probabilities. This model represents the health status of a patient by latent variables having a discrete distribution and, therefore, it may be seen as a constrained version of the latent class model. On the basis of the adopted model, we implement a hierarchical clustering algorithm aimed at assessing the actual number of dimensions measured by the questionnaire. These dimensions correspond to disjoint groups of items. Once the number of dimensions is selected, we also study the discriminating power of every item, so that it is possible to select the subset of these items which is able to provide an amount of information close to that of the full set. We illustrate the proposed approach on the basis of the data collected on 1,051 elderly people hosted in a sample of Italian nursing homes. (Contains 10 tables and 1 figure.)
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- 2012
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18. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations
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Sherratt, K., primary, Gruson, H., additional, Grah, R., additional, Johnson, H., additional, Niehus, R., additional, Prasse, B., additional, Sandman, F., additional, Deuschel, J., additional, Wolffram, D., additional, Abbott, S., additional, Ullrich, A., additional, Gibson, G., additional, Ray, EL., additional, Reich, NG., additional, Sheldon, D., additional, Wang, Y., additional, Wattanachit, N., additional, Wang, L., additional, Trnka, J., additional, Obozinski, G., additional, Sun, T., additional, Thanou, D., additional, Pottier, L., additional, Krymova, E., additional, Barbarossa, MV., additional, Leithäuser, N., additional, Mohring, J., additional, Schneider, J., additional, Wlazlo, J., additional, Fuhrmann, J., additional, Lange, B., additional, Rodiah, I., additional, Baccam, P., additional, Gurung, H., additional, Stage, S., additional, Suchoski, B., additional, Budzinski, J., additional, Walraven, R., additional, Villanueva, I., additional, Tucek, V., additional, Šmíd, M., additional, Zajícek, M., additional, Pérez Álvarez, C., additional, Reina, B., additional, Bosse, NI., additional, Meakin, S., additional, Di Loro, P. Alaimo, additional, Maruotti, A., additional, Eclerová, V., additional, Kraus, A., additional, Kraus, D., additional, Pribylova, L., additional, Dimitris, B., additional, Li, ML., additional, Saksham, S., additional, Dehning, J., additional, Mohr, S., additional, Priesemann, V., additional, Redlarski, G., additional, Bejar, B., additional, Ardenghi, G., additional, Parolini, N., additional, Ziarelli, G., additional, Bock, W., additional, Heyder, S., additional, Hotz, T., additional, E. Singh, D., additional, Guzman-Merino, M., additional, Aznarte, JL., additional, Moriña, D., additional, Alonso, S., additional, Álvarez, E., additional, López, D., additional, Prats, C., additional, Burgard, JP., additional, Rodloff, A., additional, Zimmermann, T., additional, Kuhlmann, A., additional, Zibert, J., additional, Pennoni, F., additional, Divino, F., additional, Català, M., additional, Lovison, G., additional, Giudici, P., additional, Tarantino, B., additional, Bartolucci, F., additional, Jona Lasinio, G., additional, Mingione, M., additional, Farcomeni, A., additional, Srivastava, A., additional, Montero-Manso, P., additional, Adiga, A., additional, Hurt, B., additional, Lewis, B., additional, Marathe, M., additional, Porebski, P., additional, Venkatramanan, S., additional, Bartczuk, R., additional, Dreger, F., additional, Gambin, A., additional, Gogolewski, K., additional, Gruziel-Slomka, M., additional, Krupa, B., additional, Moszynski, A., additional, Niedzielewski, K., additional, Nowosielski, J., additional, Radwan, M., additional, Rakowski, F., additional, Semeniuk, M., additional, Szczurek, E., additional, Zielinski, J., additional, Kisielewski, J., additional, Pabjan, B., additional, Holger, K., additional, Kheifetz, Y., additional, Scholz, M., additional, Bodych, M., additional, Filinski, M., additional, Idzikowski, R., additional, Krueger, T., additional, Ozanski, T., additional, Bracher, J., additional, and Funk, S., additional
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- 2022
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19. Answering Two Biological Questions with a Latent Class Model via MCMC Applied to Capture-Recapture Data
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Bartolucci, F., Mira, A., Scaccia, L., Di Bacco, M., editor, D’Amore, G., editor, and Scalfari, F., editor
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- 2004
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20. Valutazione e classificazione degli impatti e distribuzione delle specie alloctone in Italia
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Montagnani, C., Gentili, R., Brundu, G., Celesti‐grapow, L., Galasso, G., Lazzaro, L., Armeli Minicante, S., Carnevali, L., Acosta, A. T. R., Agrillo, E., Alessandrini, A., Angiolini, C., Ardenghi, N. M. G., Arduini, I., Armiraglio, S., Attorre, F., Bacchetta, G., Bagella, S., Barni, E., Barone, G., Bartolucci, F., Beretta, A., Berta, G., Bolpagni, R., Bona, I., Bonari, G., Bouvet, D., Bovio, M., Briozzo, I., Brusa, G., Buldrini, F., Buono, S., Burnelli, M., Carboni, M., Carli, E., Casella, F., Castello, M., Ceriani, R. M., Cianfaglione, K., Cicutto, M., Conti, F., Dagnino, D., Domina, G., Fanfarillo, E., Fascetti, S., Ferrario, A., Ferretti, G., Foggi, B., Gariboldi, L., Giancola, C., Gigante, D., Guarino, R., Iamonico, D., Iberite, M., Kleih, M., Laface, V. L. A., Latini, M., Lazzeri, V., Lozano, V., Magrini, S., Mainetti, A., Marinangeli, F., Martini, F., Masiero, F., Massimi, M., Mazzola, L., Medagli, P., Mugnai, M., Musarella, C. M., Nicolella, G., Orsenigo, S., Peccenini, S., Pedullà, L., Perrino, E. V., Plutino, M., Podda, L., Poggio, L., Posillipo, G., Proietti, C., Prosser, F., Ranfa, A., Rempicci, M., Rivieccio, G., Rodi, E. S., Rosati, L., Salerno, G., Santangelo, A., Scalari, F., Selvaggi, A., Spampinato, G., Stinca, A., Turcato, C., Viciani, D., Vidali, M., Villani, M., Vurro, M., Wagensommer, R. P., Wilhalm, T., and Citterio, S.
- Published
- 2022
21. Baker’s cyst in pediatric patients: Ultrasonographic characteristics
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Alessi, S., Depaoli, R., Canepari, M., Bartolucci, F., Zacchino, M., and Draghi, F.
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- 2012
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22. A Tempered Expectation-Maximization Algorithm for Latent Class Model Estimation
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Brusa, L, Bartolucci, F, Pennoni, F, Perna, C, Salvati, N, Schirripa Spagnolo, F, Brusa, L, Bartolucci, F, and Pennoni, F
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annealing, finite mixture models, latent variables, local maxima ,SECS-S/01 - STATISTICA - Abstract
We consider maximum likelihood estimation of the Latent Class (LC) model, which is formulated through individual discrete latent variables. We explore tempering techniques to overcome the problem of multimodality of the log-likelihood function. A Tempered Expectation-Maximization (T-EM) algorithm is proposed, which can adequately explore the parameter space and reach the global maximum more frequently than the standard EM algorithm. We assess the performance of the proposed approach by a Monte Carlo simulation study and an application based on data about anxiety and depression in oncological patients.
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- 2021
23. Hidden Markov and regime switching copula models for state allocation in multiple time-series
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Bartolucci F., Pennoni F., Cortese F., Giovanni C Porzio, Carla Rampichini, Chiara Bocci, Bartolucci, F, Pennoni, F, and Cortese, F
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daily log-returns, expectation-maximization algorithm, forecast, latent variables, model-based clustering - Abstract
We consider hidden Markov and regime-switching copula models as approaches for state allocation in multiple time-series, where state allocation means the prediction of the latent state characterizing each time occasion based on the observed data. This dynamic clustering, performed under the two model specifications, takes the correlation structure of the time-series into account. Maximum likelihood estimation of the model parameters is carried out by the expectation-maximization algorithm. For illustration we use data on the market of cryptocurrencies characterized by periods of high turbulence in which interdependence among assets is marked.
- Published
- 2021
24. A Comparison of Recent EM Accelerators within Item Response Theory
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Bartolucci, F., Forcina, A., Stanghellini, E., Payne, Roger, editor, and Green, Peter, editor
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- 1998
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25. Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates
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Bartolucci, F., Farcomeni, A., and Pennoni, F.
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- 2014
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26. Correction: Modelling the long-term health impact of COVID-19 using Graphical Chain Models.
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Gourgoura, K., Rivadeneyra, P., Stanghellini, E., Caroni, C., Bartolucci, F., Curcio, R., Bartoli, S., Ferranti, R., Folletti, I., Cavallo, M., Sanesi, L., Dominioni, I., Santoni, E., Morgana, G., Pasticci, M. B., Pucci, G., and Vaudo, G.
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POST-acute COVID-19 syndrome ,COVID-19 - Abstract
This document is a correction notice for an article titled "Modelling the long-term health impact of COVID-19 using Graphical Chain Models" published in BMC Infectious Diseases. The correction states that a brief heading was mistakenly introduced in the title during typesetting. The correct title of the article is "Modelling the long-term health impact of COVID-19 using Graphical Chain Models." The original article has been corrected. The correction notice also includes the names of the authors involved in the study. [Extracted from the article]
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- 2024
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27. Modelli univariati e multivariati per serie storiche di conteggi con applicazione a COVID-19
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Bartolucci, F, Pennoni, F, Mira, A, Bartolucci, F, Pennoni, F, and Mira, A
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Negative binomial distribution, Poisson model, Markov Chain Monte Carlo, Multinomial autoregressive model ,SECS-S/01 - STATISTICA ,Distribuzione Binomiale Negativa, Modello di Poisson, Markov Chain Monte Carlo, Modello multinomiale autoregressivo - Abstract
Sin dai primi giorni della diffusione dell’epidemia COVID-19 in Italia, stiamo sviluppando e confrontando diversi modelli per l’analisi di serie storiche di conteggi. In particolare, ci siamo focalizzati su modelli in grado di fornire previsioni stabili in tempo reale, pur disponendo di esigue osservazioni che vengono aggiornate giorno per giorno e che possono presentare problemi dovuti a questioni legate alla rilevazione dei dati. In particolare, è noto che in alcune situazioni e a causa dell’emergenza, i dati riferiti a più giorni di rilevazione sono stati accumulati in uno stesso giorno di pubblicazione creando delle irregolarità per le serie storiche osservate. Inizialmente abbiamo utilizzato un modello di Poisson ed un modello basato sulla distribuzione Binomiale Negativa per analizzare le serie storiche univariate dei pazienti malati o in una specifica condizione in Italia o in alcune regioni, in particolare in Lombardia. Tali modelli sono stati formulati includendo un trend temporale attraverso dei polinomi e delle spline e anche includendo delle componenti autoregressive del primo e del secondo ordine. Inoltre, per tenere conto dell’effetto dei provvedimenti emanati dal Governo e dalle regioni, sono state introdotte delle covariate di intervento o dei nodi nel caso di utilizzo di spline per il trend temporale. In seguito, ci siamo resi conto che l’interesse della collettività, così come dei decisori pubblici, è quello di conoscere quante persone si ammalano e, nello stesso tempo, quante persone hanno bisogno delle diverse tipologie di assistenza, con particolare riferimento alla terapia intensiva. Abbiamo quindi pensato ad un approccio multivariato basato su un modello Multinomiale autoregressivo che considera simultaneamente le diverse tipologie di pazienti ed include anche i deceduti e i guariti come categorie di osservazione. Questo modello permette di studiare le transizioni tra diverse categorie esclusive, giorno per giorno, e di prevedere in modo attendibile, almeno nel breve termine, il fabbisogno di posti letto in ospedale ed in terapia intensiva. È interessante notare che il modello assume una struttura Markoviana avente degli stati assorbenti, come ovviamente quello dei decessi, pur essendo stimato sulla base dei dati a livello aggregato, ovvero delle distribuzioni marginali delle tabelle di contingenza relative al numero di pazienti che transitano da una categoria all’altra in giorni consecutivi. Si presta inoltre ad essere interpretato come modello epidemiologico in linea con i più comuni modelli SEIR (Susceptible – Exposed – Infected – Recovered) ed è in grado di fornire una stima di indicatori di diffusione dell’infezione collegati con il numero di riproduzione di base (R0, numero medio di persone contagiate da una persona infetta). I parametri vengono stimati utilizzando l’approccio Bayesiano attraverso un algoritmo di tipo Markov chain Monte Carlo che permette di ricavare, in forma simulata, la distribuzione a posteriori di questi parametri. L’algoritmo che abbiamo implementato è basato su due passi che vengono iterati ripetutamente e che contemplano l’utilizzo di regole di accettazione di tipo Metropolis-Hastings. L’inferenza Bayesiana è particolarmente vantaggiosa se si intende utilizzare anche le informazioni a priori, quando disponibili, derivanti da altri paesi dove l’epidemia si è sviluppata in precedenza. Nel caso di COVID-19, in particolare, è vantaggioso utilizzare i dati della Cina da dove è partita la diffusione del virus. È inoltre possibile fornire intervalli di credibilità per i parametri ed effettuare confronti tra modelli con e senza effetti di intervento per valutare l’efficacia degli stessi. Gli esercizi di cross-validation che abbiamo effettuato in questi giorni hanno permesso di evidenziare interessanti risultati in termini di affidabilità previsionale nel breve termine. Auspichiamo che la proposta del modello Multinomiale autoregressivo possa essere utilizzata come strumento di ausilio per coloro che si trovano ad affrontare questa emergenza ed intendiamo continuare a perfezionare la proposta per renderla fruibile a tutta la comunità scientifica, anche mettendo a disposizione un apposito pacchetto di facile utilizzo nell’ambiente R. From the beginning of the spread of the novel coronavirus that emerged in Lombardy (Italy), at the end of February 2020, we are developing and comparing different models for time series with count data. In particular, we are considering models able to provide stable predictions with real time data, having only few observations updated day by day. The official data may have problems due to the collection process that is made from the local healthcare public company of the local area for each of the 21 Italian regions. In particular, during this emergency some date referred to more days of collection have been officialised in a single day, causing irregularity on the observed time series. We used a Poisson model and a model based on a Negative Binomial distribution to consider the univariate time series of the official COVID-19 data provided by the Civil Protection Agency and in particular of the patients in a specific condition such as in intensive care at national or at regional level. The models have been formulated with a temporal trend through polynomials and splines. We fitted the models considering also autoregressive components of first and second order. We introduced intervention effects through suitable covariates or nodes for the splines as to account for the effects of the disease control made by the local and national authorities on the temporal trend. With the time passing we realized that a main interest of the community, and of the policy makers is to know daily the number of ill patients, and at the same time, how many people need to be hospitalized and need especially intensive care. We developed a multivariate Multinomial autoregressive model to account simultaneously for different typologies of patients and as observational categories also for those deceased and recovered. The proposed model allows us to study the transitions between different mutually exclusive typologies of individuals day by day and it provides reliable predictions of the future daily counts in each category. We notice that the model assumes a Markovian structure with absorbing states, as that of deceased, even if it is estimated according to aggregate data. Therefore, we employ the marginals of the contingency table with respect to the patients transit from one category to another. The proposed model is also suitable to be interpreted as an epidemiological model in line with the more common SIRD (Susceptible-Infective-Recovered-Dead) models. It is able to provide an estimate of the reproducibility number (R0, average number of people infected by a person with the virus). The model parameters are estimated through a Bayesian approach with the algorithm Markov Chain Monte Carlo which allows us to dispose of the simulated posterior probabilities of the model parameters. The implemented algorithm is based on two steps which are iteratively repeated where at the second step the acceptance rule defined by the proposal of Metropolis-Hastings is considered. The Bayesian approach is particularly suitable when prior probabilities are available. In the context of COVID-19, we use data provided by China from where the spreading process of COVID-19 started. This approach allows us to dispose of credibility intervals for the parameters and to compare models with and without interventions to evaluate the efficacy of the restrictive measures due to context-specific interventions. A substantial very good prediction within a short term is demonstrated by exercises of Bayesian leave-one-out cross-validation performed during these days. The proposal of the Multinomial autoregressive model is an attempt to provide an instrument to support decisions during the emergency and we intend to continue to develop the model to make it suitable and available for the scientific community also by providing a friendly R package.
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- 2020
28. Diversity in socio-economic growth at country level: a multivariate hidden Markov model
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Pandolfi, S, Bartolucci, F, Pennoni, F, Serafini, A, Pandolfi, S, Bartolucci, F, Pennoni, F, and Serafini, A
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SECS-S/01 - STATISTICA ,continuous outcomes, countries inequalities, dynamic clustering, missing data - Abstract
We use data derived from the World Bank and UNESCO Institute for Statis- tics, which are referred to 217 countries followed for 18 years, from 2000 to 2017, to study the persistence of economic conditions at country level over time. We consider several variables, such as GDP per capita, educational levels, life expectancy at birth, infant mortality rate, school enrollment, government expenditure in education, and the value of the Gini index that measures the inequality level of the distribution of income through household survey data (Schultz, 1998). These variables are related to the human development index proposed by the United Nations Development Programme (1990) for measuring the well-being at country level. The proposed approach allow us to charac- terize disparities among countries in a dynamic fashion so as to evaluate the changes in inequality over time by estimating a multivariate hidden Markov (HM) model (Bartolucci, Farcomeni, and Pennoni, 2013; Zucchini, MacDonald, and Langrock, 2016), also known as latent Markov model. In particular we pro- vide a dynamic clustering of the countries on the basis of the observed economic variables and, in turn, this permits to analyze inequalities in the development level. The resulting clustering is obtained by relying on a model tailored for longitudinal data rather than by predetermined threshold values as currently adopted by many international organizations to classify countries. The HM model assumes the existence of an unobservable process, which follows a Markov chain with a discrete number of latent states, affecting the distribution of the observed variables. In particular, we consider multivariate continuous re- sponses that, for the same time occasion, are assumed to be correlated accord- ing to a specific variance-covariance matrix, even conditionally on the latent states. In such a context, maximum likelihood estimation of model parameters is straightforward by means of the Expectation-Maximization (EM) algorithm (Dempster, Laird, and Rubin, 1977). Missing observations represent a relevant problem in this analysis, where values for some countries may be not available. Therefore, in this work, we also propose an approach for inference with missing data based on modifying the standard steps of the EM algorithm in a suitable way. In particular, we assume a structure of missing at random (MAR) re- sponses (Little and Rubin, 2002) where the missing patterns are independent of the missing responses given all the observed data. The resulting EM algorithm provides exact maximum likelihood estimates of the model parameters. Since the resulting latent states may be ordered according to the average values of the economic variables, inequalities among countries are detected for each year on the basis of the estimated posterior probabilities of the countries of be- longing to each latent state. In this way, we also explore patterns of disparities among countries and track their evolution from a dynamic perspective.
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- 2020
29. Multivariate Hidden Markov model: An application to study correlations among cryptocurrency log-returns
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Pennoni, F., Bartolucci, F., Forte, G., Ametrano, F., Pennoni, F, Bartolucci, F, Forte, G, and Ametrano, F
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Expectation-Maximization algorithm, latent variables, time series - Abstract
We provide an analysis of the market data of the major cryptocurrencies by summing a multivariate hidden Markov process also known as the latent Markov process. We model jointly the daily log-returns of BTC, ETH, XRP, LTC, and BCH. The observed log-returns are assumed to be correlated according to a variance-covariance matrix conditionally on a latent Markov process of first-order having a discrete number of latent states. In order to compare states according to their volatility, we estimate the specific variance-covariance matrix of each state. Maximum likelihood estimation of the model parameters is carried out by the Expectation-Maximization algorithm. The latent states can be ordered according to expected average values of the log-returns and their estimated volatility. We consider different model specifications in terms of number of latent states, which are identified in terms of expected log-returns and level of volatility. Under each considered scenario we also predict the latent state by the maximum a posteriori rule.
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- 2020
30. Lista Rossa della Flora Italiana. 2 Endemiti e altre specie minacciate
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▪ Rossi, G., Orsenigo, S., Gargano, D., Montagnani, C., Peruzzi, L., Fenu, G., Abeli, T., Alessan- drini, A., Astuti, G., Bacchetta, G., Bartolucci, F., Bernardo, L., Bovio, M., Brullo, S., Carta, A., Castello, M., Cogoni, D., Conti, F., Domina, G., Foggi, B., Gennai, M., Gigante, D., Iberite, M., Lasen, C., Magrini, S., Nicolella, G., Pinna, M. S., Poggio, L., Prosser, F., Santangelo, A., Selvaggi, A., Stinca, A., Tartaglini, N., Troia, A., Villani, M. C., Wagensommer, R. P., Wilhalm, T., Blasi, C., Rossi, G., Orsenigo, S., Gargano, D., Montagnani, C., Peruzzi, L., Fenu, G., Abeli, T., Alessandrini, A., Astuti, G., Bacchetta, G., Bartolucci, F., Bernardo, L., Bovio, M., Brullo, S., Carta, A., Castello, M., Cogoni, D., Conti, F., Domina, G., Foggi, B., Gennai, M., Gigante, D., Iberite, M., Lasen, C., Magrini, S., Nicolella, G., Pinna, M. S., Poggio, L., Prosser, F., Santangelo, A., Selvaggi, A., Stinca, A., Tartaglini, N., Troia, A., Villani, M. C., Wagensommer, R. P., Wilhalm, T., and Blasi, C
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Biodiversità ,Endemiti ,Conservazione ,Flora ,Liste Rosse IUCN ,Specie vegetali minacciate - Abstract
L’Italia è localizzata al centro del bacino Mediterraneo, uno tra i centri di biodiversità a livello globale più minacciati. La sua eterogeneità ambientale e climatica ha permesso la differenziazione di un ampio numero di specie vegetali (oltre 8,200 taxa), che tuttavia sono sottoposte ad un crescente numero di minacce principalmente legate ai cambiamenti di dinamiche socio-economiche. Pertanto, urgenti misure di conservazione sono necessarie per fermare la perdita di diversità vegetale e conservare il patrimonio naturale del nostro paese. Questo volume è il prodotto finale di un progetto iniziato nel 2012, finanziato dal Ministero dell’Ambiente e della Tutela del Territorio e del Mare, portato avanti dalla Società Botanica Italiana. Complessivamente, i criteri per la redazione delle liste rosse elaborati dalla IUCN sono stati applicati a 2.488 taxa della flora vascolare autoctona del nostro paese, al fine di valutare il loro attuale stato di conservazione e mettere in evidenza le principali minacce che incombono su di esse. Dopo la pubblicazione del primo volume (2013), che include la valutazione di tutte le specie italiane incluse nelle normative ratificate a livello nazionale (policy species), quali le specie elencate negli allegati della Direttiva 92/43/UE “Habitat” e negli allegati della Convenzione di Berna, in questo contributo è stato valutato un ulteriore gruppo di 2.191 taxa, incluse tutte le specie endemiche italiane non ancora sottoposte ad assessment. I risultati pubblicati nel presente volume hanno rilevato che 37 taxa sono estinti, estinti in natura o verosimilmente estinti a livello regionale (inclusi 11 taxa endemici), mentre 420 taxa (inclusi 228 taxa endemici) sono stati assegnati ad una categoria di rischio. Inoltre, 338 taxa (inclusi 248 taxa endemici) sono stati inseriti nella categoria DD, ovvero specie per cui i dati a disposizione sono insufficienti per una valutazione. La flora vascolare italiana è minacciata soprattutto dalla modificazione degli habitat naturali e semi-naturali, dovuta al disturbo antropico e, specialmente, ad agricoltura, turismo e sviluppo residenziale. Il numero più alto di taxa estinti o in declino è infatti localizzato nelle zone costiere e di pianura, dove gli impatti antropici e la distruzione degli ecosistemi sono più evidenti. La valutazione di circa un terzo dei taxa vegetali spontanei del nostro paese costituisce un importante passo verso la conservazione della flora italiana. Tuttavia, per mettere in atto una strategia di conservazione efficace del paesaggio naturale italiano e in particolare della flora vascolare, ulteriori ricerche ed approfondimenti sono necessari. Sarebbe infatti urgente risolvere alcune criticità tassonomiche, effettuare monitoraggi sulle specie minacciate e sviluppare strategie specifiche di conservazione in situ ed ex situ e piani di azione specie specifici.
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- 2020
31. Alcuni modelli per dati di conteggio con applicazione a Covid-19
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Bartolucci, F, Pennoni, F, Luca Ferrucci, Bartolucci, F, and Pennoni, F
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modello autoregressivo, modello lineare generalizzato, prevalenza dei casi attualmente positivi ,SECS-S/01 - STATISTICA - Abstract
Il capitolo ha l’intento di illustrare in modo divulgativo l’utilizzo dei modelli statistici per analizzare, l’andamento dei casi di pazienti affetti da COVID-19. In particolare si considera la serie degli individui attualmente positivi, che comprende coloro che sono in isolamento domiciliare, i ricoverati con sintomi e in terapia intensiva. I dati utilizzati nel presente lavoro sono quelli forniti giornalmente a livello ufficiale dal Dipartimento della Protezione Civile sull’andamento del COVID-19 nella regione Umbria e nell’intera penisola italiana. La prima classe di modelli che viene presa in esame è basata sulla distribuzione di Poisson, che è stata proposta dal matematico francese Siméon Denis Poisson (1781-1840) e poi estesa recentemente per tenere conto della correlazione temporale dei dati. La seconda classe di modelli è basata sulla distribuzione Binomiale Negativa che, pur essendo meno utilizzata della distribuzione di Poisson presenta delle peculiarità di interesse ed è stata formulata, in una versione preliminare, dal matematico Blaise Pascal (1623-1662). Infatti una formulazione particolare di questo modello è nota anche come distribuzione di Pascal. Questi modelli permettono di effettuare delle previsioni e dei confronti tra regioni o nazioni in quanto servono a regolarizzare i dati raccolti giornalmente. Nel presente capitolo si confronta l’andamento della prevalenza stimata per l’Umbria e per l’Italia.
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- 2020
32. Tassonomia integrata delle sottospecie di Armeria arenaria (Plumbaginaceae) con focus particolare sulle putative sottospecie endemiche dell’Appennino settentrionale
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Tiburtini, M., Astuti, G., Bartolucci, F., Casazza, G., Varaldo, L., DE LUCA, D., Bottigliero, M. V., Bacchetta, G., Porceddu, M., Domina, G., Orsenigo, S., and Peruzzi, L.
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- 2021
33. Seventeen ‘extinct’ plant species back to conservation attention in Europe
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Abeli, T. Albani Rocchetti, G. Barina, Z. Bazos, I. Draper, D. Grillas, P. Iriondo, J.M. Laguna, E. Moreno-Saiz, J.C. Bartolucci, F.
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humanities - Abstract
Seventeen European endemic plant species were considered extinct, but improved taxonomic and distribution knowledge as well as ex situ collecting activities brought them out of the extinct status. These species have now been reported into a conservation framework that may promote legal protection and in situ and ex situ conservation. © 2021, The Author(s), under exclusive licence to Springer Nature Limited part of Springer Nature.
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- 2021
34. Testicular adrenal rest tissue in a patient with classical congenital adrenal hyperplasia: color Doppler findings
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Depaoli, R., Bartolucci, F., and Draghi, F.
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- 2013
- Full Text
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35. Dimensionality of the Latent Structure and Item Selection Via Latent Class Multidimensional IRT Models
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Bartolucci, F., Montanari, G. E., and Pandolfi, S.
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- 2012
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36. Rejoinder on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates
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Bartolucci, F., Farcomeni, A., and Pennoni, F.
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- 2014
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37. New floristic data of vascular plants from central and southern Italy
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CONTI F, FALCINELLI F, GIACANELLI V, PAOLUCCI M, PIRONE G, PROIETTI E, STINCA A, BARTOLUCCI F, Conti, F, Falcinelli, F, Giacanelli, V, Paolucci, M, Pirone, G, Proietti, E, Stinca, A, and Bartolucci, F
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Plant Science - Published
- 2019
38. Hidden Markov models for continuous multivariate data with missing responses
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Pennoni, F, Bartolucci, F, Serafini, A, Pandolfi, S, Theodore Chadjipadelis, Pennoni, F, Bartolucci, F, Serafini, A, and Pandolfi, S
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multivariate Gaussian distribution ,SECS-S/01 - STATISTICA ,Expectation-Maximization algorithm ,forward-backward recursion ,hierarchical clustering - Abstract
Hidden Markov models represent a popular tool for the analysis of longitudinal data, allowing the dynamic clustering of sample units on the basis of a set of repeated responses. In the literature on longitudinal data analysis, these models are typically used in the presence of multivariate categorical data, that is, when more categorical responses are observed at each time occasion. These formulations rely on the assumption of local independence, according to which the responses are conditionally independent given the latent states. Such assumption also simplifies the treatment of missing responses when the missing-at-random assumption is plausible. Here, we deal with the case of continuous multivariate responses in which, as in a Gaussian mixture models, it is natural to assume that the continuous responses for the same time occasion are correlated, according to a specific variance-covariance matrix, even conditionally on the latent states. Although maximum likelihood estimation of this model is straightforward in standard cases using the Expectation-Maximization algorithm, we focus on its estimation when: (i) suitable constraints on the variance-covariance matrix are assumed; (ii) there are missing responses. The constraints we refer to are commonly adopted in the literature of Gaussian finite mixture models. Regarding the assumptions on the generation of missing data we focus on the missing-at-random assumption and we also account for possible individual covariates that may directly affect the responses (in addition to the latent states). In particular, we propose an Expectation Maximization (EM) algorithm that provides exact maximum likelihood estimates and also computes standard errors for the parameter estimates. The proposed approach is illustrated by a simulation study, to evaluate the computational load, and through a real case analysis. We also show how the proposal may be useful in a context of time-series analysis with an application to financial data. An R implementation of the proposed algorithm is made available by the authors within the LMest package.
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- 2019
39. Latent variable models for evaluation systems
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Bartolucci, F, Pennoni, F, Vittadini, G., Bini, M, Amenta, P, D'Ambra, A, Camminatiello, I, Bartolucci, F, Pennoni, F, and Vittadini, G
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Latent Class Model ,Item Response Theory ,Latent Markov model ,SECS-S/01 - STATISTICA - Abstract
A review of certain latent variable models that may be effectively used to build evaluation systems is proposed. The review is focused on the item response theory models, the latent class models and the latent Markov models. Certain latent Markov versions are illustrated in detail from the perspective of evaluation when multilevel or non-experimental longitudinal data are available.
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- 2019
40. Nomenclatural novelties and typification of names in Scorzonera sensu lato (Asteraceae, Cichorieae) for the Italian vascular flora
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Bartolucci, F., Galasso, G., and Conti, F.
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- 2020
41. L’inventario della flora spontanea italiana e il nuovo Portale della Flora d’Italia
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Galasso, G., Bartolucci, F., Conti, F., Martellos, S., Moro, A., Pennesi, R., Peruzzi, L., Pittao, E., and Nimis, P.
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- 2020
42. Lista Rossa della Flora Italiana. 2. Endemiti e altre specie minacciate
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Rossi, G., Orsenigo, S., Gargano, D., Montagnani, C., Peruzzi, L., Fenu, G., Abeli, T., Alessandrini, A., Astuti, G., Bacchetta, G., Bartolucci, F., Bernardo, L., Bovio, M., Brullo, S., Carta, A., Castello, M., Cogoni, D., Conti, F., Domina, G., Foggi, B., Gennai, M., Gigante, D., Iberite, M., Lasen, C., Magrini, S., Nicolella, G., Pinna, M. S., Poggio, L., Prosser, F., Santangelo, A., Selvaggi, A., Stinca, A., Tartaglini, N., Troia, A., Villani, M. C., Wagensommer, R. P., Wilhalm, T., and Blasi, C.
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- 2020
43. Introduction to LMest
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Bartolucci, F. and Bartolucci, F.
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- 2020
44. Point Estimation Methods with Applications to Item Response Theory Models
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Bartolucci, F., primary and Scrucca, L., additional
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- 2010
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45. The species-specific monitoring protocols for plant species of Community interest in Italy
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Ercole, S., Fenu, G., Giacanelli, V., Pinna, M. S., Abeli, T., Aleffi, M., Bartolucci, F., Cogoni, D., Conti, F., Croce, A., Domina, G., Foggi, B., Forte, T., Gargano, D., Gennai, M., Montagnani, C., Oriolo, G., Orsenigo, S., Ravera, S., Rossi, G., Santangelo, A., Siniscalco, C., Stinca, A., Sulis, E., Troia, A., Vena, M., Genovesi, P., Bacchetta, G., Ercole, S, Fenu, G, Giacanelli, V, Pinna, M, Abeli, T, Aleffi, M, Bartolucci, F, Cogoni, D, Conti, F, Croce, A, Domina, G, Foggi, B, Forte, T, Gargano, D, Gennai, M, Montagnani, C, Oriolo, G, Orsenigo, S, Ravera, S, Rossi, G, Santangelo, A, Siniscalco, C, Stinca, A, Sulis, E, Troia, A, Vena, M, Genovesi, P, Bacchetta, G, Ercole, S., Fenu, G., Giacanelli, G., Pinna, M. S., Abeli, T., Aleffi, M., Bartolucci, F., Cogoni, D., Conti, F., Croce, A., Domina, G., Foggi, B., Forte, T., Gargano, D., Gennai, M., Montagnani, C., Oriolo, G., Orsenigo, S., Ravera, S., Rossi, G., Santangelo, A., Siniscalco, C., Stinca, A., Sulis, E., Troia, A., Vena, M., Genovesi, P., Bacchetta, G., Giacanelli, V., and Pinna, M.
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Ecology ,Conservation ,EC-Habitats Directive ,Field methodologies ,Plant species monitoring ,Population size ,Settore BIO/02 - Botanica Sistematica ,Settore BIO/03 - Botanica Ambientale E Applicata ,Forestry ,Plant Science ,Ecology, Evolution, Behavior and Systematic ,Field methodologie - Abstract
The results of a project for the identification of species-specific monitoring protocols for the Italian plant species protected under the Habitats Directive (Annexes II/IV/V) are presented. The project led to the development of 118 monitoring factsheets, providing an operational guidance for 107 vascular taxa, 10 bryophytes and 1 lichen taxon. Each factsheet includes information on the species (distribution, biology, ecology, conservation status, threats, etc.) and the description of field methodologies for the detection of the two main reporting parameters, i.e. population size and habitat quality. Practical information to plan field activities are also given. Protocols were designed to address the requirements of the European reporting system with the aim to standardize future monitoring activities, optimize efforts at national scale and overcome some current problems related to data heterogeneity and discrepancies from the EC standards. More than 60 botanists collaborated to identify the best practices and to design an operational field survey format through several stages of discussion and sharing. The protocols, developed by ISPRA and Scientific Societies and shared with the Italian institutions responsible for the Directive application, were published in a dedicated National handbook. The work provides a first uniform technical basis for future national monitoring plans.
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- 2017
46. Barcoding helps threatened species: the case of Iris marsica (Iridaceae) from the protected areas of the Abruzzo (Central Italy)
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De Castro, O., primary, Del Guacchio, E., additional, Di Iorio, E., additional, Di Maio, A., additional, Di Martino, L., additional, Bartolucci, F., additional, and Conti, F., additional
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- 2020
- Full Text
- View/download PDF
47. Ellenberg Indicator Values for the vascular flora alien to Italy
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Domina, G., Galasso, G., Bartolucci, F., Guarino, R., Domina, G., Galasso, G., Bartolucci, F., and Guarino, R.
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Bioindicator ,Settore BIO/03 - Botanica Ambientale E Applicata ,Non-native flora ,Plant Science ,Plant ecology ,EIV ,EIVs - Abstract
Studies to date about plants alien to Italy have had limited focus on the ecology of this component of the flora. Ellenberg's indicator values are a useful tool to delineate the relationship between plants and environment, recognizing for each species a functional role as biological indicator; these values have been proposed for estimating the influence of the main environmental factors in determining flora and vegetation changes on a specific surface area. This contribution includes a list of 1206 taxa of plants naturalized in at least one administrative region or casual in at least three regions of Italy. In addition, some methodological considerations on the attribution and use of Ellenberg's indicator values and a comparison with the average indices for the native Italian flora are reported.
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- 2018
48. Causal effects of dynamic direct mail campaigns on customer product portfollos
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Pennoni, F, Paas, L, Bartolucci, F, Gounaris, S, Pennoni, F, Paas, L, and Bartolucci, F
- Subjects
SECS-S/01 - STATISTICA ,causal latent Markov model, customer relationship management, direct marketing - Abstract
Statistical methods currently employed for marketing are based on strong parametric assumptions to correct for endogeneity. These assumptions may com- promise the results when applied to data in which the research expects endogeneity, but this is not the case. By considering the recent advances in the literature of causal models for panel data we propose to employ a latent Markov model which is tailored to estimate causal effects of a marketing campaign and to deal dynamically with endogeneity. Our proposal does not require the strong assumption of strictly absence of unmeasured confounders. The endogeneity correction imposes no restrictions on the data and it does not bias results when the endogeneity is not in. We assess the effects of multiple mail campaigns conducted by a large European bank with the purpose of predicting customer acquisitions of financial products.
- Published
- 2018
49. Osservazioni sulla datazione delle opere di Michele Tenore
- Author
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Galasso G., Santangelo A., Bartolucci F., Società Botanica Italiana, Galasso, G., Santangelo, A., and Bartolucci, F.
- Published
- 2018
50. Causality patterns of a marketing campaign conducted over time: evidence from the latent Markov model
- Author
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Pennoni, F, Paas, L, Bartolucci, F, Abbruzzo, A, Brentari, E, Chiodi, M, Piacentino, D, Pennoni, F, Paas, L, and Bartolucci, F
- Subjects
SECS-S/01 - STATISTICA ,causal latent Markov model, customer relationship management, direct marketing, Expectation-Maximization algorithm - Abstract
Many statistical methods currently employed to evaluate the effect of a marketing campaign in dealing with observational data advocate strong parametric assumptions to correct for endogeneity among the participants. In addition, the assumptions compromise the estimated values when applied to data in which the research expects endogeneity but this is not realized. Based on the recent advances in the literature of causal models dealing with data collected across time, we propose a dynamic version of the inverse-probability-of-treatment weighting within the latent Markov model. The proposal, which is based on a weighted maximum likelihood approach, accounts for endogeneity without imposing strong restrictions. The likelihood function is maximized through the Expectation-Maximization algorithm which is suitably modified to account for the inverse probability weights. Standard errors for the parameters estimates are obtained by a nonparametric bootstrap method. We show the effects of multiple mail campaigns conducted by a large European bank with the purpose to influence their customers to the acquisitions of the addressed financial products
- Published
- 2018
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