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Composite indicators are a common choice for synthesizing complex phenomena. Over the years, they have grown in popularity and are now applied in many social and environmental sciences. Among others, a subject of increasing interest is gender equality analysis. Gender composite indicators, even if easy to read, may provide a limited picture of the problem. Here we discuss the potentiality to integrate the use of composite indicators for gender gaps with Bayesian networks, powerful tools for explaining the complex association structure in the dataset and developing scenarios to orient policy-making. An example is carried out on Italian province-level data.

In this paper, a new resampling technique for sampling designs with unequal inclusion probabilities is proposed. The basic idea is to use a resampling design based on ppswor. Its main properties are studied, and its relationships with other resampling methodologies are discussed.

In this paper, resampling techniques based on pseudo-populations in the presence of a general πps sampling design are studied.Different forms of calibrated pseudo-populations are introduced and discussed. The influence of calibration on the performance of resampling is studied via simulation

In sampling finite populations, several resampling schemes have been proposed. The common starting point is that, despite its excellent asymptotic properties, Efron’s original bootstrap only works for i.i.d. data. This condition is not met in sampling finite populations, because of the dependence among units due to the sampling design. Hence, adaptations are needed to account for the non i.i.d. nature of data. Different versions of the standard bootstrap algorithm have been proposed in the literature. A new class of resampling procedures for finite populations is defined. Such a class appears to provide a unified framework that allows for encompassing other resampling algorithms already proposed. Its main theoretical justification is based on asymptotic, large sample arguments: the probability distribution of the original statistic and its approximation based on resampling converge to the same limit. Technically speaking, it is shown that a “finite population version” of the empirical process and its “resampled form” weakly converge to the same limiting Gaussian process. In a sense, this justification is similar to those given for classical bootstrap.

A new class of sampling strategies is proposed that can be applied to population-based surveys targeting a rare trait that is unevenly spread over an area of interest. Our proposal is characterised by the ability to tailor the data collection to specific features and challenges of the survey at hand. It is based on integrating an adaptive component into a sequential selection, which aims both to intensify the detection of positive cases, upon exploiting the spatial clustering, and to provide a flexible framework to manage logistics and budget constraints. A class of estimators is also proposed to account for the selection bias, that are proved unbiased for the population mean (prevalence) as well as consistent and asymptotically Normal distributed. Unbiased variance estimation is also provided. A ready-to-implement weighting system is developed for estimation purposes. Two special strategies included in the proposed class are presented, that are based on the Poisson sampling and proved more efficient. The selection of primary sampling units is also illustrated for tuberculosis prevalence surveys, which are recommended in many countries and supported by the World Health Organisation as an emblematic example of the need for an improved sampling design. Simulation results are given in the tuberculosis application to illustrate the strengths and weaknesses of the proposed sequential adaptive sampling strategies with respect to traditional cross-sectional non-informative sampling as currently suggested by World Health Organisation guidelines.

Contains fulltext : 249831.pdf (Publisher’s version ) (Open Access) Pakistan's national tuberculosis control programme (NTP) is among the many programmes worldwide that value the importance of subnational tuberculosis (TB) burden estimates to support disease control efforts, but do not have reliable estimates. A hackathon was thus organised to solicit the development and comparison of several models for small area estimation of TB. The TB hackathon was launched in April 2019. Participating teams were requested to produce district-level estimates of bacteriologically positive TB prevalence among adults (over 15 years of age) for 2018. The NTP provided case-based data from their 2010-2011 TB prevalence survey, along with data relating to TB screening, testing and treatment for the period between 2010-2011 and 2018. Five teams submitted district-level TB prevalence estimates, methodological details and programming code. Although the geographical distribution of TB prevalence varied considerably across models, we identified several districts with consistently low notification-to-prevalence ratios. The hackathon highlighted the challenges of generating granular spatiotemporal TB prevalence forecasts based on a cross-sectional prevalence survey data and other data sources. Nevertheless, it provided a range of approaches to subnational disease modelling. The NTP's use and plans for these outputs shows that, limitations notwithstanding, they can be valuable for programme planning.

In the present paper, resampling for finite populations under an iid sampling design is reviewed. Our attention is mainly focused on pseudo-population-based resampling due to its properties. A principled appraisal of the main theoretical foundations and results is given and discussed, together with important computational aspects. Finally, a discussion on open problems and research perspectives is provided.

When observational studies are used to establish the causal effects of treatments, the estimated effect is affected by treatment selection bias. The inverse propensity score weight (IPSW) is often used to deal with such bias. However, IPSW requires strong assumptions whose misspecifications and strategies to correct the misspecifications were rarely studied. We present a bootstrap bias correction of IPSW (BC-IPSW) to improve the performance of propensity score in dealing with treatment selection bias in the presence of failure to the ignorability and overlap assumptions. The approach was motivated by a real observational study to explore the potential of anticoagulant treatment for reducing mortality in patients with end-stage renal disease. The benefit of the treatment to enhance survival was demonstrated; the suggested BC-IPSW method indicated a statistically significant reduction in mortality for patients receiving the treatment. Using extensive simulations, we show that BC-IPSW substantially reduced the bias due to the misspecification of the ignorability and overlap assumptions. Further, we showed that IPSW is still useful to account for the lack of treatment randomization, but its advantages are stringently linked to the satisfaction of ignorability, indicating that the existence of relevant though unmeasured or unused covariates can worsen the selection bias.

This paper intends to contribute to an up-to-date discussion of dual frame designs in agricultural surveys. It starts by reviewing historical scenarios of applications to envision new perspectives, and ends by presenting a modern approach to the problem. A dual frame sampling design is proposed that has the appeal of relying on low-cost technological resources. The design has enough generality to allow for applications not only on agricultural but also on rural and environmental surveys, or any other survey related to the use of soil. Unbiased estimations based on domain and multiplicity approaches are presented and their major differences are discussed. Design parameters, design feasibility by different sample size allocations, as well as the statistical performance of several dual frame estimators are investigated using a Monte Carlo simulation study that is built on information from the Brazilian agricultural census of 2006 and FAO’s Global Strategy’s field experiences in the city of Goiana, Pernambuco. The results show dual frames present a gain in precision when compared to a single area frame survey. In addition, the choice of the best design and estimator depends upon scenarios with different types of allocation and different sizes of area frame segments.

A resampling technique for probability-proportional-to size sampling designs is proposed. It is essentially based on a special form of variable probability, without replacement sampling applied directly to the sample data, yet according to the pseudo-population approach. From a theoretical point of view, it is asymptotically correct: as both the sample size and the population size increase, under mild regularity conditions the proposed resampling design tends to coincide with the original sampling design under which sample data were collected. From a computational point of view, the proposed methodology is easy to be implemented and efficient, because it neither requires the actual construction of the pseudo-population nor any form of randomization to ensure integer weights and sizes. Empirical evidence based on a simulation study 1 indicates that the proposed resampling technique outperforms its two main competitors for confidence interval construction of various population parameters including quantiles.

The FENStatS Covid19 WR is a free spontaneous association of statistical experts from 14 European countries, united by concerns related to the current pan-infodemic and statistical challenges revealed by the Covid-19 havoc. The present short paper tracks when, how and why the WG has formed, illustrates our mission, aims and scope, describes the steps taken so far and outlines perspectives for future work.

Background: The aim of the study was to investigate if a parents’ training program on nutritional and oral health behaviours held by health professionals can influence children habits in a sample of kindergartens in Northern Italy.Methods: The study designed was a longitudinal study. The study population were children aged 6–36 months attending four kindergartens. Parents were invited to participate filling out a self-administered questionnaire, and after having returned the informed consent form they were asked to participate to a training meeting. Parents had to fill out a questionnaire at baseline and after 3 months from the training meeting. The questionnaire included information on socio-demographics about parents, oral hygiene habits of parents and child, and eating habits of child. Wald test was used to analyse data collected. Results: After the training program, almost all children were able to use a toothbrush suitable for kids (from 91% at the baseline to 99% after the 3-months). The analysis shows that the given training significantly increased the number of children who use toothpaste from 86% at baseline (95%CI: 85%-88%) to 96% (95%CI: 94%-98%). The use of fluoride toothpaste significantly increased after the training intervention as the baseline measure of 59% proportion increased to 80%. The intervention study showed a positive impact on the number of pupils who wash their tooth more than twice per week and on the timing of oral hygiene as both night and morning time proportion increased after the 3-month. Regarding the feeding habits the given consultation resulted in a statistically significant increase regarding the importance of morning snack from 94% at baseline (95%CI: 92%-96%) to 97% after 3-month (95%CI: 94%-99%). Another promising effect of the education is the decrease from 47% at baseline (95%CI: 46%-49%) to 42% after the study (95%CI:41%-43%) of the bad habit of having a snack after dinner.Conclusions: Results of our study have shown that a parents training intervention can have good results on the oral hygiene and eating habits of children of this age group. To underline the importance of several professional figures who work together with a common purpose.

The number of samples is a major issue when estimating the mean herbage mass of grazed paddocks. The aim of this study was to assess the number of samples required for accurate visual estimation of mean herbage mass in relation to the herbage mass heterogeneity and size of paddocks. Data were collected across scales of space and time (273 sampling events) from paddocks on Campos grasslands in Uruguay, using the visual estimation technique. The mean herbage mass of the paddocks ranged from 270 to 6350 kg of dry matter (DM) per hectare with coefficient of variation (CV) of 0.13 to 1.26. Twenty-four events representing four levels of herbage mass hetero- geneity (CV = 0.3, 0.5, 0.7 and 1.0) × three levels of paddock size (small, 5–13 ha; medium, 41–67 ha; large, 100–140 ha) were chosen (two replicates per group), and analyzed for the probability that the estimation error exceeded 10% of the mean (10% error probability) using the bootstrap technique. The number of samples required for controlling the 10% error probability below 0.1 increased gradually from 50 to 150 per paddock as the CV increased from 0.3 to 0.7, then sharply to 350 until the CV increased to 1.0, with no effect of paddock size. Taking account of the distribution of CV (< 0.7 in nearly 80% of the events), we propose a general recommendation to take a minimum of 150 samples per paddock for accurate estimation of mean herbage mass in Campos grasslands irrespective of the size of paddocks.

Resampling methods are widely studied and increasingly employed in applied research and practice. When dealing with complex sampling designs, common resampling techniques require adjusting noninteger sampling weights in order to construct the so called “pseudopopulation” in order to perform the actual resampling. The practice of rounding, however, has been empirically shown to be harmful under general designs. In this paper, we present asymptotic results concerning, in particular, the practice of rounding resampling weights to the nearest integer, an approach that is commonly adopted by virtue of its reduced computational burden, as opposed to randomization‐based alternatives. We prove that such approach leads to nonconsistent estimation of the distribution function of the survey variable; we provide empirical evidence of the practical consequences of the nonconsistency when the point estimation of the variance of complex estimators is of interest.

Sampling a rare and clustered trait in a finite population is challenging: traditional sampling designs usually require a large sample size in order to obtain reasonably accurate estimates, resulting in a considerable investment of resources infront of the detection of a small number of cases. A notable example is the case of WHO’s tubercoulosis (TB) prevalence surveys, crucial for countries that bear a high TB burden, the prevalence of cases being still less than 1%. In the latest WHO guidelines, spatial patterns are not explicitly accounted for, with the risk of missing a large number of cases; moreover, cost and logistic constraints can pose further problems. After reviewing the methodology in use by WHO, the use of adaptive and sequential approaches is discussed as natural alternatives to overcome the limitations of the current practice. A small simulation study is presented to highlight possible advantages and limitations of these alternatives, and an integrated approach, combining both adaptive and sequential features in a single sampling strategy is discussed as a promising methodological perspective.

This paper concerns a Multivariate Latent Markov Model recently introduced in the literature for estimating latent traits in social sciences. Based on its ability of simultaneously dealing with longitudinal and spacial data, the model is proposed when the latent response variable is expected to have a time and space dynamic of its own, as an innovative alternative to popular methodologies such as the construction of composite indicators and structural equation modeling. The potentials of the proposed model and the added value with respect to the traditional weighted composition methodology, are illustrated via an empirical Gender Statistics exercise, focused on gender gap as the latent status to be measured and based on supranational o cial statistics for 30 European countries in the period 2010–2015.

Gender inequality - both in space and time - is a latent trait, namely only indirectly measurable through a collection of observable variables and indicators purposively selected. Even if composite indicators are normally used by social scientists, when measuring gender-gap they are known to have case-specific technical limitations. In this paper we propose an innovative approach based on a multivariate Latent Markov model (LMM) for the analysis of gender inequalities as measured by the aforementioned indicators

A new sampling design is derived for sampling a rare and clustered population under both cost and logistic constraints. It is motivated based on the example of national TB prevalence surveys, sponsored by WHO for high TB-burden countries and usually located in the poorest parts of the world. A Poisson-type sampling design named Poisson Sequential Adaptive (PoSA) is proposed with a twofold purpose: (i) to increase the detection rate of positive cases; and (ii) to reduce survey costs by accounting for logistic constraints at the design level of the survey. PoSA is derived by integrating both an adaptive component able to enhace detectability and a sequential component for dealing with costs and logistic constraints. An unbiased HT-type estimator for the population prevalence (mean) is derived by adjusting for both the over-selection bias and for the conditional structure induced by the sequential selection. Unbiased variance estimation in a closed form is also provided. Simulation results are presented and show a significant pontential of PoSA in improving the sampling methodology currently suggested by WHO guidelines.

Multiple-frame surveys are commonly used to deal with under-coverage bias. The use of more than one frame introduces the possibility that frames overlap leading to increased inclusion probabilities of units that appear in multiple lists. Following the guide example of a dual frame set-up (DF) in telephone surveys, this contribution presents an extensive simulation study where different types of screenings to deal with the overlap issue are compared with the proper DF approach. We empirically show that the efforts of screening do not guarantee estimators more efficient than the DF estimators that do not need screening. Moreover simulation results show that screening at sample level does not correct for the increased inclusion probability of units in both frames produced by the DF set-up nor improve efficiency. The different estimation options will be compared with regards to survey costs, amount of information required and statistical properties of the final estimates

Recent gender literature shows a growing demand of sound statistical methods for analysing gender gaps, for capturing their complexity and for exploring the pattern of relationships among a collection of observable variables selected in order to disentangle the latent trait of gender equity. In this paper we consider parametric Hierarchical Marginal Models applying to binary and categorical data, as a promising statistical tool for gender studies. We explore the potential of Chain Graphical Models, by focusing on a special smooth sub-class of models known as Graphical Models of type II as recently developed (Nicolussi in Marginal parameterizations for conditional independence models and graphical models for categorical data, 2013) , i.e. an advanced methodology for untangling and highlighting any dependence/independence pattern between gender and a set of covariates of mixed nature, either categorical, ordinal or quantitative. With respect to traditional methodologies for treating categorical variables, such as Logistic Regression and Chi-Squared test for contingency table, the proposed model lead to a full multivariate analysis, allowing for isolating the effect of each dependent variable from all other response variables. At the same time, the resulting graph offers an immediate visual idea of the association pattern in the entire set of study variables. The empirical performance of the method is tested by using data from a recent survey about sexual harassment issues inside university, granted by the Equal Opportunities Committee of the University of Milano-Bicocca (Italy).

The practice of rounding non-integer weights in bootstrapping complex samples from nite populations has been empirically shown to be potentially damaging. Indeed, the extent to which rounding can interfere with basic Bootstrap principles as well as with the formal properties of the nal Bootstrap estimates can be non-negligible and often severe. On the basis of these considerations, in our talk we derive asymptotic results that suggest under which conditions the use of rounding can be detrimental, and attempt a quantication of the unwanted eects induced by this practice with respect to some relevant performance indicators for estimators such as, for example, relative bias and relative root mean square error. Empirical results are discussed based on an extended simulative study, accounting for dierent high-entropy π-ps sampling designs in a number of dierent population scenarios.

For agricultural surveys, a Dual Frame design combining the customary area frame with an additional list frame has been suggested in the 70s (Hartley, Sankhya 1974) for reducing costs and improving estimate accuracy. The availability of 2 or more partial list frames supporting the area frame is often the case in modern applications so that a more general Multiple Frame design appears as a promising tool for agricul- tural surveys. Some major issues will be discussed such as: i) the selection from overlapping frames under a sampling design able to exploit all the available auxiliary information including sub-area sizes; and ii) a weighting system at the estimation stage, feasible for dealing with the increased probability to be selected in the final sample for farms also included in one or more lists and, at the same time, to be selected more than once, from different lists, even if this have not occurred in practice.

The use of 2 or more sampling frames, possibly partial and overlapping, is often ad- vocated to improve population coverage, e.g. in telephone surveys or when dealing with difficult-to-sample-populations. Independent selections from multiple frames can also help reducing survey costs and offer flexibility by allowing to blend differ- ent sampling designs and data collection modes in different frames. However multiple frame (MF) surveys are challenging at the estimation stage. In fact if the possible overlap among the frames used in the survey allows for avoiding resource-consuming as well as error-prone screening operations, at the same time it allows for multiple opportunities of selection of the same unit in the final sample, whether or not sample duplications actually have occured. As a consequence, the increased inclusion prob- abilities for units included into more than one frame must be dealt with in efficient estimation of population parameters. Several methods have appeared in the litera- ture since Hartley first introduced Dual and MF surveys in the 60’s [1], each under a somewhat different approach leading to different level of complexity and information needs in order to be implemented in practice. A systematic and unified principled framework to MF estimation will be illustrated, based on the multiplicity approach, on the Generalized Multiplicity-adjusted HT class (GMHT) of MF estimators [2] as well as on the amount of frame-level meta-data available for estimation purposes. The potential of the multiplicity approach as a unified principled approach to MF estimation will be discussed, by casting all the existing MF estimators into a unique class. By using auxiliary info derived from the post-stratification of frame-samples into disjoint domain-samples, a regression rapresentation leading to a GMHT-reg class will be illustrated. New proposals emerging in the process as well as future research perspective will conclude. The talk is based on recent developments of joint work with A.C Singh (Center of Excellence in Survey Sampling, NORC @University of Chicago) and summarizes the three most recent joint papers [3], [4], [5].

This fifth edition of the Guidelines for surveillance of drug resistance in tuberculosis is an updated version of earlier editions published in 1994 (1), 1997 (2), 2003 (3) and 2009 (4). The document takes into account recent advancements in laboratory diagnostics and subsequent WHO guidance, including Molecular line probe assays for rapid screening of patients at risk of multidrug-resistant tuberculosis (5), Xpert MTB/RIF assay for the diagnosis of pulmonary and extrapulmonary TB in adults and children (6) and Xpert MTB/RIF implementation manual (7). These updated guidelines also incorporate experience gained from 20 years of the Global Project on Anti-Tuberculosis Drug Resistance Surveillance (hereafter referred to as the Global Project), a project initiated by WHO and the International Union Against Tuberculosis and Lung Disease (The Union). Acknowledgements This updated edition was written by Anna Dean and Matteo Zignol. Development of this document was guided by the following panel of external experts and staff of the World Health Organization (WHO): External experts: Daniela Cirillo (San Raffaele Scientific Institute, Milan, Italy), Frank Cobelens (KNCV Tuberculosis Foundation, The Hague, The Netherlands), Ted Cohen (Yale School of Public Health, New Haven, United States), Charlotte Colvin (United States Agency for International Development (USAID), Washington DC, United States), Julia Ershova (Centers for Disease Prevention and Control, Atlanta, United States), Aleksandr Golubkov (USAID, Washington DC, United States), Arax Hovhannesyan (World Vision Armenia, Yerevan, Armenia), Maeve Lalor (Public Health England, London, United Kingdom), Fulvia Mecatti (University of Milan-Bicocca, Milan, Italy), Patrick Moonan (Centers for Disease Prevention and Control, Atlanta, United States), Amy Pietak (USAID, Washington DC, United States), Michael Selgelid (Australian National University, Canberra, Australia), Susan van den Hof (KNCV Tuberculosis Foundation, The Hague, The Netherlands), Marieke van der Werf (European Centre for Disease Prevention and Control, Stockholm, Sweden), Armand Van Deun (Institute of Tropical Medicine, Antwerp, Belgium) and Norio Yamada (Research Institute of Tuberculosis, Tokyo, Japan)

Multiple frame surveys are powerful tools for dealing with a number of non-standard sampling designs, especially for improving population coverage and response rates by controlling survey costs. In an overlap design, the probability for units in the overlap domain to be included in the final sample is increased, and as a consequence this multiplicity must be accounted for at the estimation stage. In this paper we focus on an application issue tipical of modern telephone surveys. We assume a Dual Frame set up as extensively applied in large scale surveys and official statistics and assume that the variable of interest has a Bernuolli distribu- tion. The two frames available are the frame of landline telephone numbers (frame A) and the frame of the cellular telephone numbers (frame B) which are typically overlapping. A first sample is selected from frame A and then an indipendent sample is selected from B. It is a popular device in practice to overcome the overlap issue by dropping from interviewing any unit eventually re-selected from B. However since that would regard selected units only, it appears as not sufficient in order to deal with the actual dual frame design configured. In this paper empirical evidence will be provided of the bias and increased estimators instability/loss of accuracy that may result from the practical device above of actually ignoring the overlap versus the aknowledging of the overlap between frames as in a dual frames design implying a correction to the estimators. Empirical results include a comparison of bias and efficiency with special attention to the multiplicity dual frame estimator. Different estimators will be also considered, requiring more information at the estimation stage to be collected besides the study variable(s). Simulations are carried out at different overlapping rate ranging up to the special case where one frame is a sub- set of the other. The simulation is conducted with different success probabilities and assuming units are selected with simple random sampling as well as with more complex sampling designs.

Multiple frame surveys are useful for reducing cost for given precision constraints, improving coverage (under or over) and dealing with elusive or rare populations for which a direct sampling frame may not exist. Unlike model-based coverage bias adjustments traditionally used for single-frame surveys where domains of units subject to coverage bias are not identificable, multiple frame surveys assume identifiability of such domains, and supplementary sampling frames along with multiplicity adjustments are used to deal with the coverage bias. Point and variance estimation for multiple frame surveys are somewhat challenging because of multiplicity of units due to overlapping frames, and possible duplication of units in the sample. A simple single-frame solution can be used if selected units from the supplementary frame are screened out whenever they are listed in the main frame. However, this may not be desirable in practice because a major portion of the cost is already incurred in contacting the selected unit for the screening information. Despite the practical appeal of multiple frame surveys, they have not been commonly used possibly because of non-standard complex nature and a lack of general understanding of estimation as well as lack of consensus about a preferred methodology among researchers. However, there has been a recent resurgence of interest in multiple frame due to the practical necessity of mitigating increased cost in data collection and use of non-area frames such as cell and landline telephones. In this paper, we provide a simplified and unified review of different existing methods which should help in a better understanding in choosing a suitable method in any application, and promoting more use of multiple frames in practice.

A class of generalized multiplicity-adjusted Horvitz-Thompson (GMHT) estimators was proposed by Singh and Mecatti (2011) to provide a unified and systematic approach to existing estimators motivated from considerations of available frame level information leading to separate and combined frame approaches. Under availability of information about selected units from the sampled frame only, separate frame estimators can be obtained by regression of the basic MHT estimator on predictors (or zero functions) formed from multiple estimators for overlapping domains. However, if full information regarding selection probabilities from all possible frames a unit could have been selected is available, estimators under the combined approach can be obtained by regression of the full MHT estimator on predictors from overlapping domains. The unified framework uses zero functions in regression which play a fundamental role in statistical estimation such as quasi-likelihood estimation. It allows us to propose new and improved estimators over other estimators such as optimal regression and pseudo maximum likelihood estimators while preserving their essential features. The class of GMHT-regression estimators is constructed as sums of contributions from each frame which allow for application of standard variance estimation techniques. Results based on a limited simulation study are presented to compare various estimators.

In sampling from finite populations, several resampling schemes have been proposed (cfr. [4] for a review). The common starting point is that the original bootstrap method proposed by [3] does not work for general sampling designs. The reason is that an essential condition imposed upon the bootstrap method is that both the original sample and the bootstrap sample are considered i.i.d.. If the sampling de- sign is not taken into account, classical bootstrap could not capture the dependence among units due to the complexity of sampling design. As a consequence, adapta- tions are needed to account for the non i.i.d. nature of data. Different versions of the standard bootstrap algorithm have been proposed in the literature ([1], [2], [4] and references therein). Here we propose a new resampling procedure for finite populations. The main theo- retical justification of the procedure is based on asymptotic, large sample arguments: the probability distribution of the original statistic and its approximation based on resampling converge to the same limit. Moreover, the proposed methodology ap- pears to provide a unified framework that allows for encompassing other bootstrap algorithms already proposed.

A class of generalized multiplicity-adjusted Horvitz–Thompson (GMHT) estimators was introduced by Singh and Mecatti (J. Official Stat. 27(4):633–650, 2011, JOS) to provide a unified and systematic approach to existing estimators via GMHT-Regression. The main purpose of this chapter is to present key observations that led to the development of the unified principled framework. These are based on the use of zero functions as predictors in regression which play a fundamental role in statistical estimation such as quasi-likelihood. The key observations are listed below. First, an optimal combination of two estimators of the same domain is equivalent to regression of the simple multiplicity-adjusted HT (i.e., average of the two estimators) on the zero function defined by the difference of two estimators. Second, there are two types of zero functions for each overlapping domain—one based on domain count estimates and the other based on domain total estimates. Some estimators use both types of zero functions as predictors in regression while others optimally combine first the two domain count estimates and then apply Hajek-type ratio adjustments for each sample to optimally estimated domain counts before regressing on domain total zero functions. Third, the regression need not be optimal as it can be based on a working covariance structure to obtain robust consistent estimates; this is in view of the observation that optimal regression estimators are typically unstable (i.e., with high coefficient of variation) for complex designs due to lack of adequate degrees of freedom for estimating regression parameters. Fourth, for any regression estimator, it may be better to use count zero functions via Hajek-adjustment first because the initial GMHT estimator may not be well correlated with count zero functions. Fifth, it might be preferable to use a suitable working covariance matrix than the optimal one in order to obtain a calibration form in addition to making the estimator more stable. Finally, sixth, the basic principles used in the unified framework make it possible to construct new improved estimators over other estimators in the literature. GMHT-Regression estimators are constructed as sums of contributions from each frame which allow for application of standard variance estimation techniques.

In this paper, we focus our attention on a particular composite index of gender equality, the Global Gender Gap Index (GGGI), highlighting problematics and weaknesses and proposing a different approach structured in four steps. The starting point of our analysis is to narrow the research to a small group of OECD countries: in this way it is possible to lower the gender analysis in a homogeneous socio-cultural framework and introduce a fifth dimension related to the time use. Next, to explore which variables have a greater impact on the gender gap persistence among these countries, we propose a different weighting method, based on the structural equation modeling (SEM). Through the study of official data, the effects of these steps on the final ranking of countries were then analyzed, allowing reflections from both the methodological and socio-cultural point of view., Statistica, Vol 73, No 2 (2013)

There are four main study designs for an inventory study that are presented in this chapter. Selection of study design depends on which of the main objectives an inventory study is attempting to address, as well as the available data

The chapter describes capture-recapture modelling in the context of TB incidence estimation. Familiarity with the R computing language will help to follow the example. The chapter is intended primarily for epidemiologists and statisticians who are involved in or have an interest in inventory studies

The very expression Gender Statistics calls for a double interpretation. It accounts for the popular mix-up of statistical methodology with its typical products such as indexes, tables and graphs. At the same time it implies a broader and forward-looking perspective, which is inspired by the increasing demand of gender sensitive statistical information coming from society, official agencies, economy. Gender statistics stands as a proper independent field of statistics with its own objectives and a variety of applications in social, human and life science. Concurrently an emerging necessity of appropriate equipment of methods and dissemination tools is noticeable. The paper tracks the roots and the historical development of gender statistics, reviews critically the existing indexes and practice and outlines methodological needs and research prospects

Bootstrap algorithms are simple and appealing solutions for variance estimation under a complex sampling design, however, they must account for the non-iid nature of data. Literature about bootstrapping finite population samples appears to have developed according to two major approaches. A more practical "ad-hoc" approach refers to the so-called scaling problem and is based on a data-rescaling so that, in the linear case, the resulting bootstrap estimate for the variance perfectly matches the analytic variance estimate. A more fundamental "plug-in" approach is based on the mimicking bootstrap principle and on the bootstrap population created on the basis of (original) sample data. Recent proposals suggest a direct bootstrap matching the linear case variance but avoiding any data scaling under mixed re-sampling designs. In this paper, a new perspective to the bootstrap population plug-in approach is provided that avoids the physical reconstruction of the bootstrap population. Basic sampling designs, both with and without replacement as well as unequal probability designs are considered. Focusing on probability-proportional-to-size sampling, a simulation study is conducted that compares all the approaches considered.

Data analysis and report is the final stage in the implementation of an inventory study. The final report should report should include the results of the study and the level of under- reporting determined as well as study methods, limitations and recommendations for how to improve TB surveuìillance and the extent to which PPM efforts need to be strengthened.The chapter provides an overview of how to describe and analyse the data

Con riferimento a un particolare indicatore composito di equità di genere (Global Gender Gap Index) sono evidenziati e discussi alcuni evidenti punti di debolezza, sia sotto il profilo concettuale che sotto il profilo tecnico-statistico. Con l’obiettivo di esplorare metodologie alternative che possano migliorare tali punti deboli, è proposta l’aggiunta di una quinta dimensione, di tipo sociale e l’utilizzo di una tecnica standard di analisi multivariata per la produzione dell’ordinamento fra Paesi. Vantaggi potenziali ed eventuali cambiamenti significativi nella graduatoria finale verrano esplorati empiricamente mediante uno studio di simulazione, basato su dati ufficiali e restringendo l’attenzione all’Italia e a un sottoinsieme di paesi Europei confrontabili sotto il profilo culturale e di sviluppo sociale ed economico.