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PURPOSE: Several bedside clinical tests have been proposed to predict difficult tracheal intubation. Unfortunately, when used alone, these tests show less than ideal prediction performance. Some multivariate tests have been proposed considering that the combination of some criteria could lead to better prediction performance. The goal of our research was to compare three previously described multivariate models in a group of adult patients undergoing general anesthesia. METHODS: This study included 220 patients scheduled for elective surgery under general anesthesia. A standardized airway evaluation which included modified Mallampati class (MM), thyromental distance (TMD), mouth opening distance (MOD), head and neck movement (HNM), and jaw protrusion capacity was performed before anesthesia. Multivariate models described by El-Ganzouri et al., Naguib et al., and Langeron et al. were calculated using the airway data. After anesthesia induction, an anesthesiologist performed the laryngoscopic classification and tracheal intubation. The sensitivity, specificity, and receiver operating characteristic (ROC) curves of the models were calculated. RESULTS: The overall incidence of difficult laryngoscopic view (DLV) was 12.7%. The area under curve (AUC) for the Langeron, Naguib, and El-Ganzouri models were 0.834, 0.805, and 0.752, respectively, (Langeron...>...El-Ganzouri, p...=...0.004; Langeron...=...Naguib, p...=...0.278; Naguib...=...El-Ganzouri, p...=...0.101). The sensitivities were 85.7%, 67.9%, and 35.7% for the Langeron, Naguib, and El-Ganzouri models, respectively. CONCLUSION: The Langeron model had higher overall prediction performance than that of the El-Ganzouri model. Additionally, the Langeron score had higher sensitivity than the Naguib and El-Ganzouri scores, and therefore yielded a lower incidence of false negatives.

Quantile regression is a powerful tool for learning the relationship between a response variable and a multivariate predictor while exploring heterogeneous effects. In this paper, we consider statistical inference for quantile regression with large-scale data in the "increasing dimension" regime. We provide a comprehensive and in-depth analysis of a convolution-type smoothing approach that achieves adequate approximation to computation and inference for quantile regression. This method, which we refer to as {\it{conquer}}, turns the non-differentiable quantile loss function into a twice-differentiable, convex and locally strongly convex surrogate, which admits a fast and scalable Barzilai-Borwein gradient-based algorithm to perform optimization, and multiplier bootstrap for statistical inference. Theoretically, we establish explicit non-asymptotic bounds on both estimation and Bahadur-Kiefer linearization errors, from which we show that the asymptotic normality of the conquer estimator holds under a weaker requirement on the number of the regressors than needed for conventional quantile regression. Moreover, we prove the validity of multiplier bootstrap confidence constructions. Our numerical studies confirm the conquer estimator as a practical and reliable approach to large-scale inference for quantile regression. Software implementing the methodology is available in the \texttt{R} package \texttt{conquer}., An R package conquer for fitting smoothed quantile regression is available in CRAN, https://cran.r-project.org/web/packages/conquer/index.html

We introduce a multivariate stochastic volatility model that imposes no restrictions on the structure of the volatility matrix and treats all its elements as functions of latent stochastic processes. Inference is achieved via a carefully designed feasible and scalable MCMC that has quadratic, rather than cubic, computational complexity for evaluating the multivariate normal densities required. We illustrate how our model can be applied on macroeconomic applications through a stochastic volatility VAR model, comparing it to competing approaches in the literature. We also demonstrate how our approach can be applied to a large dataset containing 571 stock daily returns of Euro STOXX index.

Objective To assess the concordance of tumour grade in specimens obtained from diagnostic cystoscopic biopsy and transurethral resection of bladder tumour (TURBT) and explore the risk factors of upgrading. Methods The medical records of 205 outpatients who underwent diagnostic cystoscopic biopsy before initial TURBT were retrospectively reviewed. Comparative analysis of the tumour grade of biopsy and operation specimens was performed. Tumour grade changing from low-grade (LG) to high-grade (HG) with or without variant histology was defined as upgrading. Univariate and multivariate logistic regression analyses were performed to identify the risk factors of upgrading. Results For the 205 patients, the concordance of tumour grade between biopsy and operation was 0.639. The concordance for patients who were preoperatively diagnosed with LG and HG was 0.504 and 0.912, respectively. Univariate and multivariate logistic regression analyses showed that older age, tumour multifocality, high neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and low lymphocyte-to-monocyte ratio (LMR) were significantly associated with upgrading (odds ratio [OR] ranging from 0.412 to 4.364). The area under the curve of the different multivariate models was improved from 0.752 to 0.821, and decision curve analysis demonstrated a high net benefit when NLR, LMR, and PLR were added. Moreover, the nomogram was well calibrated. Conclusions Diagnostic cystoscopic biopsy may not accurately represent the true grade of new bladder cancer, especially for outpatients with LG. Moreover, older age, tumour multifocality, high NLR, PLR, and low LMR are risk factors of upgrading, and systemic inflammatory markers improve the predictive ability.

A new family of multivariate copulas constructed from existing copulas by transforming the corresponding random variables using their conditional distributions are introduced. It is shown that for symmetrical copulas in a triangular scheme, this transformation tends to asymptotic independence.

Transportation infrastructure plays a significant role in the economic growth of developing countries where heavy vehicles act as the backbone in transporting goods and people via highways. However, presence of heavy vehicles in traffic stream lowers the overall operating speed and flow in rural highways especially at the vicinity of intersections. This research aims at developing an empirical model for estimating and predicting capacity of highway roundabouts considering both geometric and traffic parameters. Based on data collected from six roundabouts along the national highways in Bangladesh, multivariate regression method has been used to develop an empirical model as a function of entry width, rotary lane width, distance between entry and nearest exit, central island diameter, and circulating traffic flow to estimate and predict the capacity of rural highway roundabouts. For incorporating the presence of heavy vehicles in the model, all vehicle types are converted to passenger car equivalent units. A comparative study with established methods (i.e., HCM 2016, TRRL, IRC and German method) proves the superiority of the developed model in terms of predictive capacity. Furthermore, a Python based program called PyNomo, has been executed with the help of several add-on packages (e.g., numpy, scipy, pyx) to generate a compound parallel scale nomograph consisting of multiple variables using the developed empirical model. Considering the impact of heavy vehicles in the socio-economic development of a country, the nomograph will aid the practitioners and engineers as a quick and easily interpretable graphical tool for planning and designing roundabouts in rural highways.

The output gap, the difference between potential and actual output, has a direct impact on policy decisions, e.g., monetary policy. Estimating this gap and its further analysis remain the subject of controversial debates due to methodological problems. We propose a local polynomial regression combined with a Self-Exciting Threshold AutoRegressive (SETAR) model and its forecasting extension for a systematic output gap estimation. Furthermore, local polynomial regression is proposed for the (multivariate) OECD production function approach and its reliability is demonstrated in forecasting output growth. A comparison of the proposed gap to the Hodrick–Prescott filter as well as to estimations by experts from the FED and OECD shows a higher correlation of our output gap with those from those economic institutions. Furthermore, sometimes gaps with different magnitude and different positions above or below the trend are observed between different methods. This may cause competing policy implications which can be improved with our results.

The study was designed to document the phytodiversity and vegetation dynamics of Chour Valley, Hazara Division, Pakistan during 2018–19. Sum of 115 plant species belonged to 48 families were documented. The leading family was Rosaceae with 14 plant species followed by Asteraceae (11 species). The data were analyzed for classification through two way indicator species Analysis and ordination through CCA and DCA to find differences in directories of plants communities and their habitats kind. Five plants communities were identified including, Abies-Pinus-Rheum, Picea-Cyperus-Oxalis, Rumex-Urtica-Cyperus, Picea-Betula-Viburnum and Berberis-Clematis-Potentilla communities. It was observed that the distribution of species and community structure was directly correlated to altitude, slope aspect and soil characteristics. It was also noticed that forest in the study area are continuously degraded and will go in vanishing, until conservation and restoration measures are adopted in priority basis.

In this article, we introduce a covariance-mean regression model with heterogeneous similarity matrices. It not only links the covariance of responses to heterogeneous similarity matrices induced by auxiliary information, but also establishes the relationship between the mean of responses and covariates. Under this new model setting, however, two statistical inference challenges are encountered. The first challenge is that the consistency of the covariance estimator based on the standard profile likelihood approach breaks down. Hence, we propose an adjustment and develop the Z -estimation and unconstrained/constrained ordinary least squares estimation methods. We demonstrate that the resulting estimators are consistent and asymptotically normal. The second challenge is testing the adequacy of the covariance-mean regression model comprising both the multivariate mean regression and the heterogeneous covariance matrices. Correspondingly, we introduce two diagnostic test statistics and then obtain their theoretical properties. The proposed estimators and tests are illustrated via extensive simulations and an empirical example study of the stock return comovement in the US stock market.

K-means is one of ten popular clustering algorithms. However, k-means performs poorly due to the presence of outliers in real datasets. Besides, a different distance metric makes a variation in data clustering accuracy. Improve the clustering accuracy of k-means is still an active topic among researchers of the data clustering community from outliers removal and distance metrics perspectives. Herein, a novel modification of the k-means algorithm is proposed based on Tukey’s rule in conjunction with a new distance metric. The standard Tukey rule is modified to remove the outliers adaptively by considering whether the data is distributed to the left, right or even to the input data's mean value. The elimination of outliers is applied in the proposed modification of the k-means before calculating the centroids to minimize the outliers' influences. Meanwhile, a new distance metric is proposed to assign each data point to the nearest cluster. In this research, the modified k-means significantly improves the clustering accuracy and centroids convergence. Moreover, the proposed distance metric's overall performance outperforms most of the literature distance metrics. This manuscript's presented work demonstrates the significance of the proposed technique to improve the overall clustering accuracy up to 80.57% on nine standard multivariate datasets.

Many engineering products have more than one failure mode and the evolution of each mode can be monitored by measuring a performance characteristic (PC). It is found that the underlying multi-dimensional degradation often occurs with inherent process stochasticity and heterogeneity across units, as well as dependency among PCs. To accommodate these features, in this paper, we propose a novel multivariate degradation model based on the inverse Gaussian process. The model incorporates random effects that are subject to a multivariate normal distribution to capture both the unit-wise variability and the PC-wise dependence. Built upon this structure, we obtain some mathematically tractable properties such as the joint and conditional distribution functions, which subsequently facilitate the future degradation prediction and lifetime estimation. An expectation-maximization algorithm is developed to infer the model parameters along with the validation tools for model checking. In addition, two simulation studies are performed to assess the performance of the inference method and to evaluate the effect of model misspecification. Finally, the application of the proposed methodology is demonstrated by two illustrative examples.

Statistical depth functions provide a way to order the elements of a space by their centrality in a probability distribution. That has been very successful for generalizing non-parametric order-based statistical procedures from univariate to multivariate and (more recently) to functional spaces. We introduce two general definitions of statistical depth which are adapted to fuzzy data. For that purpose, two concepts of symmetric fuzzy random variables are introduced and studied. Furthermore, a generalization of Tukey's halfspace depth to the fuzzy setting is presented and proved to satisfy the above notions, through a detailed study of its properties. A. Nieto-Reyes and L. Gonzalez are supported by the Spanish Ministerio de Economía, Industria y Competitividad grant MTM2017-86061-C2-2-P. P. Terán is supported by the Ministerio de Economía y Competitividad grant MTM2015-63971-P, the Ministerio de Ciencia, Innovación y Universidades grant PID2019-104486GB-I00 and the Consejería de Empleo, Industria y Turismo del Principado de Asturias grant GRUPIN-IDI2018-000132.

Aim To establish a predictive nomogram for malignancy risk stratification of micro-calcifications (MCCs) detected on mammography. Materials and Methods Consecutive mammograms from January 2017 to March 2021 were retrospectively reviewed. Traditional clinical features were recorded and mammographic features were estimated according to the 5th BI-RADS. A nomogram was developed to graphically predict the malignancy risk based on multivariate logistic regression analysis. The discrimination and calibration performance of the prediction model was assessed. Results There were 123 cases of suspicious MCCs with final pathological results identified with a malignancy rate of 55.2%. The malignancy rates of subgroups divided according to the morphology and distribution of MCCs, age, menopausal status and the maximum diameter of MCCs were significantly different. Multivariate logistic analysis showed that a menopause status of postmenopausal, maximum diameters of MCCs ≥2 cm, the morphology of MCCs as fine pleomorphic or fine linear or branching, and the distribution of MCCs as linear or segmental were predictive of a higher probability of malignancy. A prediction nomogram was developed based on four risk factors, including menopausal status as well as the maximum diameters, distribution and morphology of the MCCs. The AUC of that nomogram was 0.839 (95%CI:0.771–0.903). Conclusion In mammography, the morphology, distribution and maximum diameter of MCCs, and the menopausal status are independent predictors of malignant suspicious MCCs and are readily available in the clinical setting. The nomogram developed in this study for individualized malignancy risk stratification of suspicious MCCs shows a reliable discrimination performance.

This paper investigates whether multivariate crash risk is priced in the cross-section of expected stock returns. Motivated by a theoretical asset pricing model, we capture the multivariate crash risk of a stock by a combined measure based on its expected shortfall and its multivariate lower tail dependence with the systematic factors of the Carhart (1997) model. We find that stocks with a high exposure to joint crashes of the market and the momentum factor bear a risk premium which is not explained by traditional linear factor models or by other downside risk measures. Our results indicate that accounting for the multivariate crash risk of established state variables helps to understand the cross-section of expected stock returns without further expanding the factor zoo.

This paper presents static and dynamic versions of univariate, multivariate, and multilevel functional time-series methods to forecast implied volatility surfaces in foreign exchange markets. We find that dynamic functional principal component analysis generally improves out-of-sample forecast accuracy. More specifically, the dynamic univariate functional time-series method shows the greatest improvement. Our models lead to multiple instances of statistically significant improvements in forecast accuracy for daily EUR-USD, EUR-GBP, and EUR-JPY implied volatility surfaces across various maturities, when benchmarked against established methods. A stylised trading strategy is also employed to demonstrate the potential economic benefits of our proposed approach., 52 pages, 5 figures, to appear at the International Journal of Forecasting

Alzheimer’s disease (AD) is the most common type of neurological disorder that leads to the brain’s cell death overtime. It is one of the major important causes of memory loss and cognitive decline in elderly subjects around the globe. Early detection and streamlining of diagnostic practices are the prime domains of the interest to the healthcare community. Machine learning (ML) algorithms and numerous multivariate data exploratory tools have been extensively used in the field of AD research. The primary purpose of this study is to present an automated classification system to retrieve information patterns. We proposed a five-stage ML pipeline, where each stage was further categorized in different sub-levels. The study relied on the Open Access Series of Imaging Studies (OASIS) database of MRI (Magnetic Resonance Imaging) brain images for the analysis. The dataset comprised of 343 MRI sessions involving 150 subjects. Three different scores namely, MMSE (Mini-Mental State Examination), CDR (Clinical Dementia Rating), and ASF (Atlas Scaling Factor) were used in the analysis. The proposed ML pipeline constitutes a classifier system along with data transformation and feature selection techniques that have been embedded inside an experimental and data analysis design. Performance metrics for Random Forest (RF) classifier showed the highest output in the classification accuracy.

Objective To develop an item response theory (IRT)–calibrated, patient-reported outcome measure (the PROMIS Cancer Function Brief 3D Profile) of physical function, including associations with fatigue and social participation, in cancer rehabilitation patients. Design Large-scale field testing, graded response model IRT analyses, and multivariate regression analysis. Setting Six cancer rehabilitation clinics associated with cancer centers across the United States. Participants Adults (N=616) treated in outpatient cancer rehabilitation medicine clinics. Intervention Not applicable. Main Outcome Measures: The PROMIS(r) Cancer Function 3D Profile (including existing items from PROMIS(r) item banks). Results A total of 616 patients completed 21 items in the initial item pool. Nine items were removed because of comparatively lower information that they provide according to the IRT item calibrations, low item-total correlations, or bimodal distributions. The remaining items generated a 12-item short form. Regression analyses determined that the items were responsive to and representative of the patient population across trait ranges and multiple domains and subdomains of function. Conclusions This psychometric investigation supports the use of the PROMIS Cancer Function Brief 3D Profile for evaluating function in outpatient cancer rehabilitation patients.

Uncertainty requires suitable techniques for risk assessment. Combining stochastic approximation and stochastic average approximation, we propose an efficient algorithm to compute the worst case average value at risk in the face of tail uncertainty. Dependence is modelled by the distorted mix method that flexibly assigns different copulas to different regions of multivariate distributions. We illustrate the application of our approach in the context of financial markets and cyber risk.

Background water shortage is the greatest significant reason controlling crop yield that harmfully affects fruit production, boll shedding, lint yield and fiber properties quality in cotton plants. Objectives: this study was carried out at Sakha Agricultural Research Station, Kafr El-Sheikh, Agric. Res. Center, Egypt, during 2014 and 2015 growing seasons. A study was undertaken in cotton (G. barbadense L.) to detect the magnitude of genetic divergence among parental cotton genotypes and their F1s cross combinations and to select most suitable combinations for tolerant water deficient. Results and conclusion: the data showed that the first eight main principal components with eigen value more than one are extracted from the complicated components, the total cumulative variance of these eight factors amount for about 85.28% of the total multivariate variance. Suggesting that these PCA scores might be used to summarize the original variables in any further analysis of the data. The female parent 10,229 formed wide cluster having wide divergent distance from the other groups. On the same trend, the extra-long varieties Giza 96 and Giza 45 formed unique groups, cluster number 8 and cluster 1 nearly related. Cluster number 7 and cluster 4 which consisted of two genotypes for each characterized by susstable to water deficit conditions. The cross combinations Giza 77 x Dandra, Giza 77 x Pima S6, Giza 96 x Australy and Minufy x Dandra grouped at the same cluster, however the cross combinations Giza 86 x Dandra and Giza 68 × 10,229 classified into two different clusters. The data revealed that the inter cluster distance was higher than the intra cluster, indicating wide genetic divergent among the studied genotypes. Principal components analysis is useful in identifying and most influential characters affecting genetic variation of population.

Micro abstract This large retrospective study using the data from the Surveillance, Epidemiology, and End Results found a similar distribution of 21-gene recurrence score, similar trends of chemotherapy regarding 21-gene recurrence score, and similar prognostic effect of the 21-gene recurrence score between those with Chinese and White American breast cancer. Purpose To compare the distribution, chemotherapy-decision making, and prognosis of the 21-gene recurrence score (RS) between Chinese breast cancer (BC) in the United States and White American (WA) BC. Methods We identified early-stage and estrogen receptor-positive BC patients diagnosed between 2004 and 2015. Multivariate logistic regression, Kaplan-Meier analysis, and multivariate Cox proportional hazards models were used for statistical analyses. Results A total of 67486 patients were identified, including 66215 (98.1%) WA patients and 1271 (1.9%) Chinese patients. Regarding the RS, 38894 (57.6%) had low RS, 23882 (35.4%) had intermediate RS, and 4710 (7.0%) had high RS. A similar distribution of RS was found between WA and Chinese BC (P=0.280). The race was not the predictor associated with high RS. Similar trends of chemotherapy use were found in Chinese and WA BC. In WA BC, there were 4.1%, 31.5%, and 72.2% of patients receiving chemotherapy in low, intermediate, and high RS cohorts, respectively (P Conclusion Our results demonstrate similar distribution, chemotherapy use, and outcome of the 21-gene RS between Chinese and WA BC in the United States.

The forecasting of time series provides great convenience in our daily life. Studies of time series forecasting have been used in many fields such as financial models, weather, and traffic patterns. In this paper, we propose a model fusion-based time series forecasting to improve the forecasting accuracy and efficiency. We propose a time series forecasting scheme based on a multivariate grey model and uses artificial fish swarm algorithm to optimize the settings. We then propose two fusion models with the grey model-based schemes on two different perspectives: data decomposition, and weighted summation. We conduct evaluations based on real data series and compared them with other forecasting models. Results show that our model can achieve good prediction accuracy and efficiency, which can be used for time series forecasting in different scenarios.

Background Radiomics is a powerful tool for automatic extraction of morphologic features, but when applied to cerebral aneurysms is inferior to established descriptors in classifying rupture status. We sought a strategy to recover neck orientation and parent vessel caliber in a bid to enhance Radiomics performance and facilitate its adoption for aneurysm risk stratification. Methods 135 sidewall (32 ruptured) and 216 bifurcation (90 ruptured) aneurysms from 3-dimensional rotational catheter angiography (3DRA) datasets were analyzed. Clinical 3DRA defined in arbitrary orientation underwent affine transformations enabling aneurysm neck alignment to XY plane prior to analysis in PyRadiomics, thus facilitating automatic extraction of aneurysm height and width, previously not possible with random alignment. Additionally, parent vessel size was estimated from aneurysm location, and incorporated into enhanced Radiomics (height, width, height/width, size ratio). Rupture status classification was compared across methodologies, for 31 automatically computed conventional Radiomics, enhanced Radiomics, and established size/shape descriptors using univariate, multivariate, and area under the curve (AUC) statistics. Results Enhanced Radiomics derived Height/Width and Size Ratio were significantly higher in both ruptured subsets. Using multivariate analysis in sidewall lesions, enhanced Radiomics (AUC=0.85) matched established features (AUC=0.86) and outperformed conventional Radiomics (AUC=0.82); in bifurcation lesions enhanced Radiomics (AUC=0.78) outperformed both established features (AUC = 0.76) and conventional Radiomics (AUC=0.74). Conclusion Enhanced Radiomics incorporating neck orientation and parent vessel estimate is an efficient operator-independent methodology which offers superior rupture status classification for both sidewall and bifurcation aneurysms and should be considered a strong candidate for larger scale multi-center and multi-modality validation.

In this paper we introduce a multivariate family of distributions for multivariate count data with excess zeros, which is a multivariate extension of the univariate zero-inflated Bell distribution. We derive various general properties of this multivariate distribution. In particular, the marginal distributions are univariate zero-inflated Bell distributions. The model parameters are estimated using the traditional maximum likelihood estimation method. In addition, we develop a simple EM algorithm to compute the maximum likelihood estimates of the parameters of the new multivariate distribution with closed-form expressions for the maximum likelihood estimators. Empirical applications that employ real multivariate count data are considered to illustrate the usefulness of the new class of multivariate distributions, and comparisons with the multivariate zero-inflated Poisson distribution, multivariate zero-adjusted Poisson distributions, and multivariate zero-inflated generalized Poisson distribution are made.

Introduction Multivariate time series prediction of infectious diseases is significant to public health, and the deep learning method has attracted increasing attention in this research field. Material and methods An adaptively temporal graph convolution (ATGCN) model, which learns the contact patterns of multiple age groups in a graph-based approach, was proposed for COVID- 19 and influenza prediction. We compared ATGCN with autoregressive models, deep sequence learning models, and experience- based ATGCN models in short-term and long-term prediction tasks. Results Results showed that the ATGCN model performed better than the autoregressive models and the deep sequence learning models on two datasets in both short-term (12.5% and 10% improvements on RMSE) and long-term (12.4% and 5% improvements on RMSE) prediction tasks. And the RMSE of ATGCN predictions fluctuated least in different age groups of COVID- 19 (0.029 ± 0.003) and influenza (0.059±0.008). Compared with the Ones-ATGCN model or the Pre-ATGCN model, the ATGCN model was more robust in performance, with RMSE of 0.0293 and 0.06 on two datasets when horizon is one. Discussion Our research indicates a broad application prospect of deep learning in the field of infectious disease prediction. Transmission characteristics and domain knowledge of infectious diseases should be further applied to the design of deep learning models and feature selection. Conclusions The ATGCN model addressed the multivariate time series forecasting in a graph-based deep learning approach and achieved robust prediction on the confirmed cases of multiple age groups, indicating its great potentials for exploring the implicit interactions of multivariate variables.

OBJECTIVES: The aims of this study are to (1) create clusters of joint temporal patterns of diet and physical activity (PA), and (2) determine the association of these clusters with health status indicators including body mass index (BMI), waist circumference (WC), fasting plasma glucose, hemoglobin A1c, triglyceride, high-density lipoprotein cholesterol, total cholesterol, blood pressure and health outcomes including obesity, Type 2 Diabetes (T2DM) and metabolic syndrome (MS) in U.S. adults 20–65 years. METHODS: A random day of PA from accelerometry data and the first day 24 hour dietary recall collected in the National Health and Nutrition Examination Survey 2003–2006 were used to determine absolute PA intensity, absolute energy intake, and the time of these activities. Dietary and PA data from 1,627 U.S. adults were Z-normalized. Dynamic time warping (DTW) coupled with kernel-k means clustering algorithm was used to develop joint temporal dietary and PA patterns that maximally partition individuals with similar temporal behaviors into mutually exclusive clusters derived from the data rather than predefined standards. Multivariate regression models adjusted for ptential confounders, multiple comparisons and survey design factors determined associations between joint temporal patterns and health status indicators along with health outcomes (P

We generalize the concept of Pascal matrices to matrices associated with sets of points R ⊆ Z ≥ 0 n by considering multidimensional binomial coefficients as entries. We study their properties and prove that the infinite matrix associated with the set R = Z ≥ 0 n is in fact an element of the multivariate Riordan group.

Wall shear stress (WSS), the spatial gradient of flow velocity at luminal surface, has been employed for aneurysmal hemodynamic analysis, but it is sensitive to surface irregularities and noise. We devised a volumetric approach to evaluate discriminant power of intra-dome flow velocity distribution and modal analysis in rupture status determination compared with previously described WSS analysis.Catheter three-dimensional rotational angiographic datasets matched for volume were segmented in 20 sidewall aneurysms (10 ruptured), computational fluid dynamics simulations were performed, and velocity distributions were extracted from mesh-independent isometric sampling followed by moment analysis (mean, variance, skewness, and kurtosis). Univariate and multivariate analysis was used to evaluate discriminant performance of velocity moments. Sensitivity of velocity moments and WSS was evaluated to bleb presence and surface irregularity using digital bleb removal and surface noise addition.Velocity moments of ruptured aneurysms showed higher skewness (2.45 ± 0.57 vs. 1.36 ± 0.82, P = 0.003) and kurtosis (11.83 ± 4.77 vs. 6.05 ± 4.65, P = 0.01) with lower mean (0.019 ± 0.01 vs. 0.038 ± 0.02, P = 0.03) compared with unruptured lesions; in multivariate modeling, skewness alone emerged as best predictor (area under the curve = 0.88). Bleb removal increased low WSS by 548%, and surface noise decreased it by 85.8% while having a smaller (7%) effect on velocity skewness and kurtosis.High aneurysm dome flow velocity skewness and kurtosis suggest an exponential distribution in ruptured lesions, with high peaks at low velocities, consistent with areas of slow flow. In contrast to WSS-based techniques, this approach is robust against surface variations, with promising improved rupture status discriminant performance that requires further validation in expanded future studies.

A new stop frequency model was developed to predict the daily number of maintenance activity stops made by individual household heads during a typical weekday. This new model was based on the modification of a conventional multivariate ordered probit (MOP) model by maintaining the probit assumption for the marginal distributions while introducing non-normal dependence among the error terms using copula functions. Therefore, the copula-based MOP model would relieve the restriction of imposing joint normality on the error terms in the conventional MOP model. The new MOP model would not only account for the intra-household interactions in stop-making decisions, but also allow the best functional form to be determined for representing dependencies among household heads. Using the New York Metropolitan Transportation Council's 2010/2011 regional household travel survey data, the copula-based MOP model was employed to examine stop-making behavior for individual household heads residing in New York City and its adjacent counties in Mid-Hudson Valley and New Jersey. Empirical results provided useful insights into the observed effects of socio-demographics, land use density, transportation service, and work schedule together with potential unobserved common effects on the inter-relatedness of spousal stop-making decisions at the household level. The results show that the MOP model with a Clayton copula structure provides the best data fits and there is a very strong positive dependence among error terms of stop-making equations. Furthermore, the dependence among the maintenance activity propensities of household heads is asymmetric, with a stronger tendency of household heads to simultaneously have low maintenance activity levels than to simultaneously have high maintenance activity levels.

Aim: There lacks a predictive model for overall survival (OS) of node-negative perihilar cholangiocarcinoma (PHC). This study aimed at developing and validating a prognostic nomogram to predict OS of node-negative PHC after resection. Methods: We established a nomogram via multivariate regression analysis by using the design cohort (n = 410, obtained from Surveillance, Epidemiology, and End Results database), and its external verification was done in the validation cohort (n = 100, the First Affiliated Hospital of Sun Yat-sen University). Predictive accuracy of the nomogram was assessed by concordance-index (C-index), calibration curves, and decision curve analysis (DCA). Performance of the nomogram was compared with the American Joint Committee on Cancer (AJCC) staging system. Results: Multivariate regression analysis revealed that age, tumor grade, and the count of examined lymph nodes were independent prognostic factors for OS of node-negative PHC. The nomogram had a C-index of 0.603 and 0.626 in design cohort and validation cohort, respectively, which was better than that of AJCC staging system (both p

The omnipotent instrument for modeling multivariate dependence of random variables in standard probability theory has become copulas discovered by A. Sklar in 1959. Only recently Omladic and Stopar prove that in the bivariate case an analogous role is played by exactly the same copulas for random variables coming from finitely additive probability spaces. One of the main results of this paper is that this is true also in the general multivariate case. The extension to n dimensions requires a better understanding of quasi-copulas, the lattice closure of copulas with respect to the pointwise order. We need to develop a new equivalent definition of this notion that should be useful in other applications as well. Another tool we introduce and seems to be new even in the standard probability approach, is multivariate quasi-distributions. We also expand the coherence theory for quasi-copulas and quasi-distributions to the multivariate situation. Finally, our main result is a multivariate Sklar type theorem in the imprecise setting.

We present new versions of the CLT which are valid for each ergodic homogeneous multivariate random field ( X 1 ( ⋅ ) , … , X d ( ⋅ ) ) on R m or Z m ( m ≥ 1 ) (in particular, for each ergodic stationary random process and for each ergodic stationary random sequence) such that for all l E [ | X l ( 0 ) | 2 + δ ] ∞ for some δ > 0 ( l = 1 , . . . , d ) ; in some statements ergodicity is not assumed. Randomization made it possible to significantly weaken the strong mixing conditions and other restrictions of dependence, that are imposed in the conventional CLTs. These results pave the way to consistent statistical inference for homogeneous random fields and stationary processes with strong dependence.

With the development of wind turbine (WT) operation and maintenance technologies, the condition monitoring (CM) method based on the data of supervisory control and data acquisition (SCADA) systems has become one of the most popular and cheapest ways to detect the anomalies. As the operation condition of WT will change over time, it is necessary to real-time update the CM model with little manual maintenance to ensure its long-term performance. Therefore, a SCADA data-driven WTCM method based on incremental learning and multivariate state estimation technique (MSET) is proposed. Firstly, for the memory matrix (MM) of MSET, a similarity-based sample selection method is proposed, which is simpler and more effective than the previous method. Secondly, based on the proposed sample selection method, an incremental learning strategy for MSET is proposed, which can add the normal new data in MM and remove the redundant data in real-time. Aiming at the slow computation speed caused by the large MM, a dynamic down-sampling method is proposed, which makes only half of the data participate in the real-time computation and can reduce the computation time by at least 80%. About three years SCADA data of two different types of WTs are used to verify the proposed method. The experimental results show that the incremental MSET can maintain higher estimation accuracy and lower false alarm rate in long-term operation, and can detect the potential gearbox faults hours to weeks in advance.

Objective To develop and validate a nomogram for predicting the risk of venous thromboembolism in critically ill patients with primary intracerebral hemorrhage. Methods Patients ≥18 years old with primary intracerebral hemorrhage were screened within 24 hours of onset from January 2019 to April 2021. Univariate and multivariate logistic regression analyses were performed to screen out independent predictors that were significantly associated with venous thromboembolism. A nomogram was constructed based on the results of a multivariate regression analysis. Discrimination and calibration were used to evaluate performance of the nomogram. A decision curve analysis was used to assess its clinical utility. Results This study enrolled 369 patients. The nomogram included 3 predictors from the regression analysis: D-dimer, National Institutes of Health Stroke Scale score, and Glasgow Coma Scale score on admission. The area under the receiver operating characteristic curve was 0.794, indicating good discrimination of the nomogram. The nomogram demonstrated calibration curves with slight deviation from the ideal predictions. Decision curve analysis showed that the prediction nomogram was clinically useful. Conclusions This nomogram comprising D-dimer, National Institutes of Health Stroke Scale score and Glasgow Coma Scale score on admission can accurately predict the risk of venous thromboembolism in critically ill patients with intracerebral hemorrhage.

Objetivo: Valorar la utilidad del CURB-65 para predecir la mortalidad a 30 días en pacientes adultos hospitalizados con COVID-19. Métodos: Cohorte realizada entre el 1 de marzo y el 30 de abril 30 de 2020 en Ecuador. Resultados: Se incluyeron 247 pacientes (edad media 60 ± 14 años, 70% varones, mortalidad global 41,3%). Los pacientes con CURB-65 ≥2 presentaron mayor mortalidad (57% vs. 17%, p

Purpose The likelihood of valproate (VPA) induced thrombocytopenia increases with higher VPA levels. In critically ill patients, the biological active free VPA level cannot be predicted from the total serum level. In this study, we evaluated the relationship between trough free VPA serum levels and concomitant platelet counts and assessed risk factors for the development of thrombocytopenia with the aim of generating a formula specifying the probabilities of developing thrombocytopenia based on trough free serum VPA levels. Methods Trough free VPA levels and concomitant platelet counts were collected from a large cohort of patients who participated in a prospective VPA monotherapy trial. Significant variables associated with thrombocytopenia in a univariate analysis were evaluated in a multivariate model. A receiver operator curve was performed to compute the trough free VPA levels with the greatest discriminating power in predicting thrombocytopenia. Results 844 trough free VPA levels and concomitant platelet counts obtained from 264 patients were analyzed. In a multivariate analysis, free trough VPA levels, gender, and baseline platelet counts were significantly associated with thrombocytopenia. Using stepwise regression and multivariate logistic regression analyses, we generated gender specific formulas for predicting platelet counts and probabilities of developing thrombocytopenia. The trough free VPA with the greatest discriminating power to predict platelet values ≤ 100,000/μL was 16.65 µg/mL. Conclusions The generated model was based on trough free VPA levels and achieved high sensitivity and specificity. Our results are therefore generalizable and can be applied to estimate the probability of developing thrombocytopenia in critically ill patients.

Coal is an important energy source worldwide. Objectively and accurately predicting coal consumption is conducive to healthy coal industry development, because such predictions can provide references and warnings that are useful in formulating energy strategies and implementing environmental policies. Population size and area economic development are the main factors that affect coal consumption. Considering the above influences, this paper first establishes a differential equation and proposes a novel multivariable Verhulst grey model (MVGM(1,N)) based on grey information differences. MVGM(1,N) extends classical model from single-variable to multivariate and diminishes the characteristics of Verhulst’s reliance on saturated S-shaped and single-peak data, making classical model more applicable to real situations. To prove the effectiveness of MVGM(1,N) simulation experiments are carried out in areas with high coal consumption. The result of this proposed model is more precise than that of NLARX, ARIMA and five classical grey models Finally, this novel multivariable model predicates coal consumption of Inner Mongolia and Gansu Provinces in China, the results show that MVGM(1,N) is preferable to other models, indicating that this model can effectively predict coal consumption.

Our aim was to test a novel method for estimating the age of young calves, using serum γ-glutamyl transferase (GGT) activity, total protein concentration and immunoglobulin G (IgG) concentration. Blood samples were taken from 59 dairy calves at 2, 4, 6, 8, 10, 12, and 14 days of age, and serum GGT activity, total protein concentration, and IgG concentration were measured. Bayesian regression models were used to estimate the association of GGT, total protein, and IgG, with calf age. A multivariate hierarchical regression model was then created, and the model's reliability in estimating the age of simulated subjects was assessed. The univariate models showed a strong within-calf relationship between age and GGT (conditional R2 (cR2) = 0.93), and age and total protein (cR2 = 0.75), while the relationship between calf age and IgG was less consistent (cR2 = 0.63). There was a high degree of variation between calves for these parameters, particularly for the relationship between age and total protein (marginal R2 (mR2) 0.02), and age and IgG (mR2 0.01); somewhat less between-calf variation was seen for GGT (mR2 0.30). For the final multivariate model, we tested the model reliability by simulating new subjects. The credible intervals for estimates of calf age generated from the model were wide, indicating poor reliability. We concluded that single measurements of serum GGT activity, total protein concentrations and IgG concentrations cannot be used to reliably estimate the age of young calves to within 1–2 days.

Recent technological advancements and developments have led to a dramatic increase in the amount of high-dimensional data and thus have increased the demand for proper and efficient multivariate regression methods. Numerous traditional multivariate approaches such as principal component analysis have been used broadly in various research areas, including investment analysis, image identification, and population genetic structure analysis. However, these common approaches have the limitations of ignoring the correlations between responses and a low variable selection efficiency. Therefore, in this article, we introduce the reduced rank regression method and its extensions, sparse reduced rank regression and subspace assisted regression with row sparsity, which hold potential to meet the above demands and thus improve the interpretability of regression models. We conducted a simulation study to evaluate their performance and compared them with several other variable selection methods. For different application scenarios, we also provide selection suggestions based on predictive ability and variable selection accuracy. Finally, to demonstrate the practical value of these methods in the field of microbiome research, we applied our chosen method to real population-level microbiome data, the results of which validated our method. Our method extensions provide valuable guidelines for future omics research, especially with respect to multivariate regression, and could pave the way for novel discoveries in microbiome and related research fields.

One of the major tasks of monitoring production well operations is to determine bottom-hole flowing pressure. The overwhelming majority of wells in the Perm Krai are serviced using borehole pumps, which makes it difficult to take direct bottom-hole flowing pressure measurements. The bottomhole filtration pressure (BHFP) in these wells is very often determined by recalculating the parameters measured at the well mouth (annulus pressure, dynamic fluid level depth). The recalculation is done by procedures based on analytically determining the characteristics of the gas-liquid mixture in the wellbore, which is very inconsistent to perform due to the mixture's complex behavior. This article proposes an essentially different approach to BHFP measurements that relies on the mathematical processing of the findings of more than 4000 parallel mouth and deep investigations of the oil production wells of a large oil-production region. As a result, multivariate mathematical models are elaborated that allow reliably determining the BHFP of oil-production wells in operation.

Purpose The treatment of medication-related osteonecrosis of the jaw (MRONJ) is greatly challenging for surgeons. In this study, we reviewed patients with MRONJ treated in our hospital in the past 5 years and explored the risk factors of recurrence. Methods A retrospective cohort study was conducted to review the patients with MRONJ from January 1, 2016 to December 31, 2020. All patients received a monthly intravenous application of zoledronic acid. The primary outcome variable was the treatment outcome during follow-up. The predictor variables were local and systemic factors related to the treatment outcome. Multivariate logistic regression analysis was performed to identify the risk factors of recurrence after MRONJ treatment. Results A total of 58 patients (62 sites) were included in this study. In multivariate regression analysis, the risk factor associated with recurrence after MRONJ treatment was the duration of medication of more than 18 months (odds ratio = 7.346; 95% confidence interval: 1.461-36.946; P = .016). Conclusions Using zoledronic acid over 18 months may increase the risk of recurrence in MRONJ patients.

The semiparametric estimation of multivariate fractional processes based on the tapered periodogram of the differenced series is considered in this paper. We construct multivariate local Whittle estimators by incorporating the maximal efficient taper developed by Chen (2010). The proposed estimation method allows a wide range of potentially nonstationary long-range dependent series, being invariant to the presence of deterministic trends with the same extent of the differencing order, without a two-step procedure. We establish the consistency and asymptotic normality of the proposed estimators, which have no discontinuities, and show that the asymptotic variance is the same as that of the nontapered local Whittle estimation by increasing the order of a taper to infinity with a moderately slow rate. We examine the finite sample behavior of the proposed estimators through a simulation experiment.

Objectives Systemic lupus erythematosus (SLE) is a chronic, autoimmune disease that may cause physical and functional disability. The objective of this study is to measure prevalence and estimate incremental cost of SLE treatment using information from administrative databases in Colombia. Methods We use data from the patients on the Colombian contributive health system with a period of study from 2015 to 2017. The incremental cost of SLE is estimated using a matched study by propensity score and multivariate balance of covariates. To reduce the effect of possible specification problems, we use Extreme Gradient Boosting, a flexible machine learning algorithm. We use paired t statistical comparison and Bootstrap to validate the robustness of the method. In addition, we use a machine learning regression approach on the cost of control patients to achieve double robustness and compare the results. Results SLE prevalence ranges between 41.65 and 54.47 (cases/100 000), which is lower than other Latin American countries. Using the operative definition of SLE, 5527 patients were selected. The potential control sample was composed of 1 942 253 patients. The total annual direct estimated cost per patient was US $2172. Adjusted incremental cost was US $1662. Considering 4 severity classes of SLE, the cost ranges from US $8823 for severe to US $447 for mild cases. Conclusions Incremental costs of SLE in Colombia are similar to those from other middle-income countries. Compared with high-income countries, the cost is lower; nevertheless, if it is calculated proportional to the per capita health expenditure, it is comparable.

Introduction Preeclampsia (PE) affects 2–8% of pregnancies and is one of the main causes of maternal morbidity and mortality worldwide. Early identification of pregnant women at higher risk for PE would allow the use of interventions to reduce adverse maternal and perinatal outcomes. Objective To assess the ability of bioelectrical impedance analysis (BIA) in pregnancy to predict the development of PE. Methods This prospective cohort involved healthy nulliparas who underwent BIA at 17–20 weekś gestation and were followed until delivery. We used univariate and multivariate logistic regression to assess the ability of BIA measures to predict the occurrence of PE. We used an adjusted regression model to estimate the probability of developing PE, the Hosmer-Lemeshow test to assess the adequacy of the final model, and ROC curves to assess the sensitivity and specificity of different BIA measures in the prediction of PE. Results Twelve (6.1%) of the 196 participants developed PE. In the final multivariate model, the following BIA measures were associated with the occurrence of PE: extracellular water/intracellular water ≤ 0.618, skeletal muscle mass ≥ 25 Kg, and body fat percentage ≥ 44%. The combination of these three measures had a predictive accuracy of 83.7%, a sensitivity of 83.3%, a specificity of 83.7%, and a negative predictive value of 98.7% for PE. Conclusion BIA done on nulliparous women at 17–20 weekś gestation has a good accuracy and high negative predictive value for the risk of developing PE.

The current study investigated how people summarize and represent objects with multiple features to cope with the complexity due to the number of objects and feature dimensions. We presented a set of circles whose color and size were either correlated perfectly (r = 1) or not correlated at all (r = 0). Using a membership identification task, we found that participants formed a statistical representation that included information about conjunctions as well as each color and size dimensions. In addition, we found that participants represented different set boundaries depending on the correlation between features of a set. Lastly, a pair-matching task revealed that participants predicted one feature value from the other feature value based on the correlation between features of a set. Our findings suggest that people represent a multi-feature ensemble statistically as a multivariate feature distribution, which is an efficient strategy to cope with scene complexity.

We described here the assemblage patterns accompanying the soft-bottom amphipods on both spatial and vertical scales. During winter 2020, 6820 individuals were collected from Hurghada and Shalateen, the Red Sea coast from five different depths ranging from 0.5 to 35 m. Twenty two amphipod species were recorded of which 18 (82 %) and 9 (41%) species were from Hurghada and Shalateen, respectively. Perioculodes aequimanus was the most abundant sand amphipod amounting to 17% and 39% in Hurghada and Shalateen, respectively, followed by Ceradocus rubromaculatus (27%) in Shalateen, and Metaphoxus sp. and Bemlos sp. (15%) in Hurghada. Depth influenced the total amphipod abundance in Hurghada, while, in Shalateen it showed an opposite assemblage pattern. The multivariate approach based on Non-Metric Multi-Dimensional Scaling (MDS) was helpful to detect differences between the study sites against depths. Sandy bottom composition, temperature, and depth variations were the main factors that influenced the amphipod assemblages according to the PCA and ANOSIM outputs. The SIMPER analysis showed that the amphipod Lysianassa ceratina and Lembos sp. were the most contributing species to the dissimilarity between the two study sites, while Guernea petalocera was the most contributing species to the similarity between Hurghada and Shalateen.

Introduction The association between the level of physical activity and quality of cardio-pulmonary resuscitation (CPR) performed by laypeople is unclear. The aim of this study was to evaluate the associations between physical activity level and laypeople performance during an eight-minute scenario of CPR. Materials and methods This study was a secondary analysis of the MANI-CPR Trial. The entire cohort of participants was grouped based on the level of physical activity assessed using the International Physical Activity Questionnaire (IPAQ) into a “low-moderate” level group and a “high” level group. Descriptive statistics were used for unadjusted analysis and multivariate logistic and linear regression models were also performed. Results A total of 492 participants who reached the score of “Advanced CPR performer” at the 1-min final test monitored by Laerdal Resusci Anne QCPR were included in this analysis; 224 with a low-moderate level and 268 with a high level of physical activity. A statistically significant difference was found for the outcome of percentage of compressions with adequate depth (low-moderate group: 87.8% [41·4%-99·3%], high group: 97% [63·2%–100%]; P = 0·003). No associations remained significant after controlling for biometric characteristics of the participants, compression protocols and sex. Conclusion Adequate quality CPR may not need high baseline level of physical activity to be performed by a lay rescuer.

Background and objectives Physical education (PE) classes are among the main intervention strategies for increasing levels of physical activity (PA) to adolescent's health improvements. However, low levels of psychological satisfaction in physical education classes (PE satisfaction) and multiple associations with some factors as sex, age, sedentarism, sports practice, sleep, quality of life can act as moderators of levels of physical activity. Considering these aspects, this study aims to propose a theoretical model of multivariate relationships to verify the association between PE satisfaction with PA levels, considering the contribution of sports practice, health indicators, age, and sex on these relations. Methods A cross-sectional study with 470 adolescents (230 boys) aged 11–17 years from the south of Brazil. Several questionnaires were applied to measure the study variables. The theoretical/statistical support of the structural equation model was evaluated according to fit parameters and strength of relations. Results Sports practice, health indicators, age, and sex were mediators of the relationship between PE satisfaction and PA levels. Conclusion There is a positive, however indirect, relationship between PE satisfaction with the levels of physical activity in adolescents, with greater strength of association in boys and at younger ages. It was identified that the practice of sport contributes to the main mediator factor for all relationships beneficial to the health of adolescents in the multivariate model. This means that adolescents who practiced sports showed greater PE satisfaction, more appropriate health indicators, associated with higher levels of physical activity as compared to nonpractitioners of sport.

Background The relationship between neighborhood environments and suicide has not been well studied. In the present study, relations between suicidal ideation and social and physical environments were investigated in Korean adults. Methods This nationwide cross-sectional study was conducted using data from the 2013 Korea Community Health Survey. The final analysis included 221,038 individuals aged ≥ 19 years. A multivariate regression model was used to examine the independent effects of environments on suicidal ideation. Results Approximately 9.4% of participants had experienced suicidal ideation. Stepwise multivariate regression analysis showed that trusting neighbors and all five physical environments were independently associated with suicidal ideation. As regards physical environments, relations with suicidal ideation varied across subgroups: satisfaction with overall safety was to be strongly and consistently related to less suicidal ideation. Limitations Causality and the impact of unmeasured confounders were not addressed. Conclusion Neighborhood environments may reduce suicidal ideation.