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Climate change has become increasingly important in recent years. It is the outcome of the burning of fossil fuels that increased the concentration of atmospheric carbon dioxide (CO 2 ), over the last century. Mitigating the impacts of climate change requires a better understanding and assessment of the countries' economic decisions on the amount of CO 2 emissions. This paper assesses the variability between the different countries in the trends of CO 2 emissions and electricity consumption from 1975 to 2014, while identifying clusters of countries of similar trends over time. The novel methodology applied in this paper enables us to assess long-debated issues in climate literature. The temporal dynamic effects of electricity consumption and economic growth on CO 2 emissions across countries are studied using functional data analysis (FDA) methods. The latter have proven to be useful tools for visualising similarities and differences in the non-linear trends of CO 2 emissions without forcing linear trends and stationary relationships which can be unrealistic and misleading. The results indicate the possibility of identifying changes in the trends of CO 2 emissions and electricity consumption for a wide range of heterogeneous countries over the study period. The findings also reveal that economic growth puts a strain on the environment, where many high-income countries are still away from attaining economic-energy sustainability. [ABSTRACT FROM AUTHOR]

The histogram has all its bin widths equal to some non-random number arbitrary set by an analyst (EBWH). In the result, particular bin counts are random variables. This paper presents also a histogram that is constructed in a converse manner. Bin counts are all equal to some non-random number arbitrary set by an analyst (EBCH). In the result, particular bin widths are random variables. The first goal of the paper is a choose of constant bin width (of bin numbers k) in the EBWH, which maximize the similarity measure in the Monte Carlo simulation. The second goal is a choose of constant bin count in the EBCH, which maximize the similarity measure in the Monte Carlo simulation. The third goal is to present similarity measures between empirical and theoretical data. The fourth goal is the comparative analysis of two histogram methods by means of the frequency formula. The first additional goal is a tip how to proceed in EBCH when modulo(n,k)≠0. The second additional goal is the software in the form of a Mathcad file with the implementation of EBWH and EBCH. [ABSTRACT FROM AUTHOR]

In this paper, reparameterization and student-t are applied to Stochastic Volatility (SV) model. We aim to reduce the amount of autocorrelation of the SV parameters and to introduce heavy-tailed model via the Bayesian computation of the Markov Chain Monte Carlo (MCMC) samplers. This research paper helps support better MCMC estimation of the SV model for volatile Asian FX series during Covid-19. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has spread seriously throughout the world. Predicting the spread, or the number of cases, in the future can facilitate preparation for, and prevention of, a worst-case scenario. To achieve these purposes, statistical modeling using past data is one feasible approach. This paper describes spatio-temporal modeling of COVID-19 case counts in 47 prefectures of Japan using a nonlinear random effects model, where random effects are introduced to capture the heterogeneity of a number of model parameters associated with the prefectures. The negative binomial distribution is frequently used with the Paul-Held random effects model to account for overdispersion in count data; however, the negative binomial distribution is known to be incapable of accommodating extreme observations such as those found in the COVID-19 case count data. We therefore propose use of the beta-negative binomial distribution with the Paul-Held model. This distribution is a generalization of the negative binomial distribution that has attracted much attention in recent years because it can model extreme observations with analytical tractability. The proposed beta-negative binomial model was applied to multivariate count time series data of COVID-19 cases in the 47 prefectures of Japan. Evaluation by one-step-ahead prediction showed that the proposed model can accommodate extreme observations without sacrificing predictive performance. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of classification of misaligned multivariate functional data. We propose to use a model-based approach for the joint registration and classification of such data. The observed functional inputs are modeled as a functional nonlinear mixed effects model containing a nonlinear functional fixed effect constructed upon warping functions to account for curve alignment, and a nonlinear functional random effects component to address the variability among subjects. The warping functions are also modeled to accommodate common effect within groups and the variability between subjects. Then, a functional logistic regression model defined upon the representation of the aligned curves and scalar inputs is used to account for curve classification. EM-based algorithms are developed to perform maximum likelihood inference of the proposed models. The identifiability of the registration model and the asymptotical properties of the proposed method are established. The performance of the proposed procedure is illustrated via simulation studies and an analysis of a hyoid bone movement data application. The statistical developments proposed in this paper were motivated by the hyoid bone movement study, the methodology is designed and presented generality and can be applied to numerous areas of scientific research. [ABSTRACT FROM AUTHOR]

When the data are stored in a distributed manner, direct application of traditional hypothesis testing procedures is often prohibitive due to communication costs and privacy concerns. This paper mainly develops and investigates a distributed two-node Kolmogorov–Smirnov hypothesis testing scheme, implemented by the divide-and-conquer strategy. In addition, this paper also provides a distributed fraud detection and a distribution-based classification for multi-node machines based on the proposed hypothesis testing scheme. The distributed fraud detection is to detect which node stores fraud data in multi-node machines and the distribution-based classification is to determine whether the multi-node distributions differ and classify different distributions. These methods can improve the accuracy of statistical inference in a distributed storage architecture. Furthermore, this paper verifies the feasibility of the proposed methods by simulation and real example studies. [ABSTRACT FROM AUTHOR]

Aiming at the problem of fault detection in data collection in wireless sensor networks, this paper combines evolutionary computing and machine learning to propose a productive technical solution. We choose the classical particle swarm optimization (PSO) and improve it, including the introduction of a biological population model to control the population size, and the addition of a parallel mechanism for further tuning. The proposed RS-PPSO algorithm was successfully used to optimize the initial weights and biases of back propagation neural network (BPNN), shortening the training time and raising the prediction accuracy. Wireless sensor networks (WSN) has become the key supporting platform of Internet of Things (IoT). The correctness of the data collected by the sensor nodes has a great influence on the reliability, real-time performance and energy saving of the entire network. The optimized machine learning technology scheme given in this paper can effectively identify the fault data, so as to ensure the effective operation of WSN. [ABSTRACT FROM AUTHOR]

In many situations, it is common to have more than one observation per experimental unit, thus generating the experiments with repeated measures. In the modeling of such experiments, it is necessary to consider and model the intra-unit dependency structure. In the literature, there are several proposals to model positive continuous data with repeated measures. In this paper, we propose one more with the generalization of the beta prime regression model. We consider the possibility of dependence between observations of the same unit. Residuals and diagnostic tools also are discussed. To evaluate the finite-sample performance of the estimators, using different correlation matrices and distributions, we conducted a Monte Carlo simulation study. The methodology proposed is illustrated with an analysis of a real data set. Finally, we create an R package for easy access to publicly available the methodology described in this paper. [ABSTRACT FROM AUTHOR]

The formulation of variable selection has been widely developed in the Bayesian literature by linking a random binary indicator to each variable. This Bayesian inference has the advantage of stochastically exploring the set of possible sub-models, whatever their dimension. Bayesian selection approaches, appropriate for categorical predictors, are generally beyond the scope of the standard Bayesian selection of regressors in the linear model since all levels of a categorical variable should be jointly handled in the selection procedure. For categorical covariates, new strategies have been developed to detect the effect of grouped covariates rather than the single effect of a quantitative regressor. In this paper, we review three Bayesian selection methods for categorical predictors: Bayesian Group Lasso with Spike and Slab priors, Bayesian Sparse Group Selection and Bayesian Effect Fusion using model-based clustering. The motivation behind this paper is to provide detailed information about the implementation of the three Bayesian selection methods mentioned above, appropriate for categorical predictors, using the JAGS software. Selection performance and sensitivity analysis of the hyperparameters tuning for prior specifications are assessed under various simulated scenarios. JAGS helps user implement these three Bayesian selection methods for more complex model structures such as hierarchical ones with latent layers. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hierarchical Bayesian approach for modeling the evolution of the 7-day moving average for the number of deaths due to COVID-19 in a country, state or city. The proposed approach is based on a Gaussian process regression model. The main advantage of this model is that it assumes that a nonlinear function f used for modeling the observed data is an unknown random parameter in opposite to usual approaches that set up f as being a known mathematical function. This assumption allows the development of a Bayesian approach with a Gaussian process prior over f. In order to estimate the parameters of interest, we develop an MCMC algorithm based on the Metropolis-within-Gibbs sampling algorithm. We also present a procedure for making predictions. The proposed method is illustrated in a case study, in which, we model the 7-day moving average for the number of deaths recorded in the state of São Paulo, Brazil. Results obtained show that the proposed method is very effective in modeling and predicting the values of the 7-day moving average. [ABSTRACT FROM AUTHOR]

This paper develops an extension of spatiotemporal models that handle count data using nonhomogeneous Poisson processes. In this new proposal, we incorporate a seasonal cycle component in the definition of the intensity function to control possible effects produced by the occurrence of the event of interest in regular periods. The seasonal cycle can cause problems in estimating the shape parameter of the Weibull and generalized Goel intensity functions. This shape parameter serves to confront the research hypothesis that seeks to identify a trend in the occurrence rate of an event of interest. In the case of the Weibull intensity function, a value significantly equal to one of the shape parameters indicates a constant rate of occurrence, less than one indicates a decreasing rate, and greater than one indicates an increasing rate. In the case of the Goel intensity function, parameter values less than or equal to one indicate a decreasing occurrence rate, and values greater than one indicate the presence of a change point. We also built a spatial model using the Musa-Okumoto intensity function as an alternative to approximate counting processes for which there is a decreasing trend in the occurrence rate of the event of interest. We estimated the parameters of the proposed method from a Bayesian perspective. Finally, we fitted the proposed model and compared it with other approximations to analyze the frequency of extreme rainfall in the northern region of the states of Maranhão and Piauí in northeastern Brazil over ten years. Among the main results, we found that (1) the proposed method has proven superior in terms of fit and prediction performance than the other models, and (2) unlike other approximations, the proposed model does not detect changes in the rate of extreme rainfall occurrences. [ABSTRACT FROM AUTHOR]

To study the effect of exposure mixture on the continuous health outcomes, one can use the linear model with a weighted sum of multiple standardized exposure variables as an index predictor and its coefficient for the overall effect. The unknown weights typically range between zero and one, indicating contributions of individual exposures to the overall effect. Because the weight parameters present only when the parameter for overall effect is non-zero, testing hypotheses on the overall effect can be challenging, especially when the number of exposure variables is above two. This paper presents a working model based approach to estimate the parameter for overall effect and to test specific hypotheses, including two tests for detecting the overall effect and one test for detecting unequal weights when the overall effect is evident. The statistics are computationally easy and one can apply existing statistical software to perform the analysis. A simulation study shows that the proposed estimators for the parameters of interest may have better finite sample performance than some other estimators. [ABSTRACT FROM AUTHOR]

Clustered current status data are frequently encountered in biomedical research and other areas that require survival analysis. This paper proposes graphical and formal model assessment procedures to evaluate the goodness of fit of the additive hazards model to clustered current status data. The test statistics proposed are based on sums of martingale-based residuals. Relevant asymptotic properties are established, and empirical distributions of the test statistics can be simulated utilizing Gaussian multipliers. Extensive simulation studies confirmed that the proposed test procedures work well for practical scenarios. This proposed method applies when failure times within the same cluster are correlated, and in particular, when cluster sizes can be informative about intra-cluster correlations. The method is applied to analyze clustered current status data from a lung tumorigenicity study. [ABSTRACT FROM AUTHOR]

Distributed interval estimation in linear regression may be computationally infeasible in the presence of big data that are normally stored in different computer servers or in cloud. The existing challenge represents the results from the distributed estimation may still contain redundant information about the population characteristics of the data. To tackle this computing challenge, we develop an optimization procedure to select the best subset from the collection of data subsets, based on which we perform interval estimation in the context of linear regression. The procedure is derived based on minimizing the length of the final interval estimator and maximizing the information remained in the selected data subset, thus is named as the LIC criterion. Theoretical performance of the LIC criterion is studied in this paper together with a simulation study and real data analysis. [ABSTRACT FROM AUTHOR]

In a large-scale environmental health population study that is composed of subprojects, often different fractions of participants out of the total enrolled have measures of specific outcomes. It's conceptually reasonable to assume the association study would benefit from utilizing additional exposure information from those with a specific outcome not measured. Partial least squares regression is a practical approach to determine the exposure-outcome associations for mixture data. Like a typical regression approach, however, the partial least squares regression requires that each data observation must have both complete covariate and outcome for model fitting. In this paper, we propose novel adjustments to the general partial least squares regression to estimate and examine the association effects of individual environmental exposure to an outcome within a more complete context of the study population's environmental mixture exposures. The proposed framework takes advantage of the bilinear model structure. It allows information from all participants, with or without the outcome values, to contribute to the model fitting and the assessment of association effects. Using this proposed framework, incorporation of additional information will lead to smaller root mean square errors in the estimation of association effects, and improve the ability to assess the significance of the effects. [ABSTRACT FROM AUTHOR]

Among the models applied to analyze survival data, a standout is the inverse Gaussian distribution, which belongs to the class of models to analyze positive asymmetric data. However, the variance of this distribution depends on two parameters, which prevents establishing a functional relation with a linear predictor when the assumption of constant variance does not hold. In this context, the aim of this paper is to re-parameterize the inverse Gaussian distribution to enable establishing an association between a linear predictor and the variance. We propose deviance residuals to verify the model assumptions. Some simulations indicate that the distribution of these residuals approaches the standard normal distribution and the mean squared errors of the estimators are small for large samples. Further, we fit the new model to hospitalization times of COVID-19 patients in Piracicaba (Brazil) which indicates that men spend more time hospitalized than women, and this pattern is more pronounced for individuals older than 60 years. The re-parameterized inverse Gaussian model proved to be a good alternative to analyze censored data with non-constant variance. [ABSTRACT FROM AUTHOR]

In competitive business, such as insurance and telecommunications, customers can easily replace one provider for another, which leads to customer attrition. Keeping customer attrition rate low is crucial for companies, since retaining a customer is more profitable than recruiting a new one. As a main statistical process control (SPC) method, the CUSUM scheme is able to detect small and persistent shifts in customer attrition. However, customer attrition summaries are typically available on an uneven time scale (e.g. 4-week and 5-week 'business month'), which may not satisfy the assumptions of traditional CUSUM designs. This paper mainly develops a latent CUSUM chart based on an exponential model for monitoring 'monthly' customer attrition, under varying time scales. Both maximum likelihood and least squares methods are studied, where the former mostly performs better and the latter is advantageous for quite small shifts. We apply a Markov chain algorithm to obtain the average run length (ARL), make calibrations for different combinations of parameters, and present reference tables of cutoffs. Three more complicated models are considered to test the robustness of deviations from the initial model. Furthermore, a real example of monitoring monthly customer attrition from a Chinese insurance company is used to illustrate the scheme. [ABSTRACT FROM AUTHOR]

In this paper, the inference of multicomponent stress-strength reliability has been derived using progressively censored samples from Topp-Leone distribution. Both stress and strength variables are assumed to follow Topp-Leone distributions with different shape parameters. The maximum likelihood estimate along with the asymptotic confidence interval are developed. Boot-p and Boot-t confidence intervals are also constructed. The Bayes estimates under generalized entropy loss function based on gamma priors using Lindley's, Tierney-Kadane's approximation and Markov chain Monte Carlo methods are derived. A simulation study is considered to check the performance of various estimation methods and different censoring schemes. A real data study shows the applicability of the proposed estimation methods. [ABSTRACT FROM AUTHOR]

Skip-lot sampling plan is often applied in industries for reducing the cost and effort of the inspection of the product having excellent quality history. Consequence of skip-lot sampling plans is to reduce the cost of inspection so which are more attractive in economical aspect. In this paper, we develop a sampling plan by incorporating the idea of resampling in two-level skip lot sampling plan and the new plan is designated as SkSP-2L.1-R. This paper presents the Markov chain formulation of the proposed plan along with the derivation of performance measures of the plan. We also provide the designing methodology to determine the optimal parameters of the SkSP-2L.1-R plan so as to minimize the average sample number by using two points on the operating characteristic curve approach. By contemplating various combinations of producer and consumer quality levels along with respective risks, a table is constructed to determine the optimal parameters. An industrial application of the proposed SkSP-2L.1-R plan is discussed. The SkSP-2L.1-R with single sampling plan as a reference plan is compared with the conventional single sampling plan, SkSP-2 plan and SkSP-2-R plan and proved that the proposed SkSP-2L.1-R plan outperforms these plans. [ABSTRACT FROM AUTHOR]

The Cash statistic, also known as the C statistic, is commonly used for the analysis of low-count Poisson data, including data with null counts for certain values of the independent variable. The use of this statistic is especially attractive for low-count data that cannot be combined, or re-binned, without loss of resolution. This paper presents a new maximum-likelihood solution for the best-fit parameters of a linear model using the Poisson-based Cash statistic. The solution presented in this paper provides a new and simple method to measure the best-fit parameters of a linear model for any Poisson-based data, including data with null counts. In particular, the method enforces the requirement that the best-fit linear model be non-negative throughout the support of the independent variable. The method is summarized in a simple algorithm to fit Poisson counting data of any size and counting rate with a linear model, by-passing entirely the use of the traditional χ 2 statistic. [ABSTRACT FROM AUTHOR]

The C statistic, also known as the Cash statistic, is often used in astronomy for the analysis of low-count Poisson data. The main advantage of this statistic, compared to the more commonly used χ 2 statistic, is its applicability without the need to combine data points. This feature has made the C statistic a very useful method to analyze Poisson data that have small (or even null) counts in each resolution element. One of the challenges of the C statistic is that its probability distribution, under the null hypothesis that the data follow a parent model, is not known exactly. This paper presents an effort towards improving our understanding of the C statistic by studying (a) the distribution of C statistic for a fully specified model, (b) the distribution of Cmin resulting from a maximum-likelihood fit to a simple one-parameter constant model, i.e. a model that represents the sample mean of N Poisson measurements, and (c) the distribution of the associated Δ C statistic that is used for parameter estimation. The results confirm the expectation that, in the high-count limit, both C statistic and Cmin have the same mean and variance as a χ 2 statistic with same number of degrees of freedom. It is also found that, in the low-count regime, the expectation of the C statistic and Cmin can be substantially lower than for a χ 2 distribution. The paper makes use of recent X-ray observations of the astronomical source PG 1116+215 to illustrate the application of the C statistic to Poisson data. [ABSTRACT FROM AUTHOR]

Comparative lifetime experiments are of particular importance in production processes when one wishes to determine the relative merits of several competing products with regard to their reliability. This paper confines itself to the data obtained by running a joint progressive Type-II censoring plan on samples in a combined manner. The problem of Bayesian predicting failure times of surviving units is discussed in details when parent populations are exponential. Two real data sets are analyzed in order to illustrate all the inferential procedures developed here. When destructive experiments under a censoring scheme finished, the researchers are usually interested to estimate remaining lifetimes of surviving units for sequel experiments. Findings of this paper are useful for these purposes specially when samples are non-homogeneous such as those taken from industrial storages. [ABSTRACT FROM AUTHOR]

Ecological momentary assessment (EMA) studies investigate intensive repeated observations of the current behavior and experiences of subjects in real time. In particular, such studies aim to minimize recall bias and maximize ecological validity, thereby strengthening the investigation and inference of microprocesses that influence behavior in real-world contexts by gathering intensive information on the temporal patterning of behavior of study subjects. Throughout this paper, we focus on the data analysis of an EMA study that examined behavior of intermittent smokers (ITS). Specifically, we sought to explore the pattern of clustered smoking behavior of ITS, or smoking 'bouts', as well as the covariates that predict such smoking behavior. To do this, in this paper we introduce a framework for characterizing the temporal behavior of ITS via the functions of event gap time to distinguish the smoking bouts. We used the time-varying coefficient models for the cumulative log gap time and to characterize the temporal patterns of smoking behavior, while simultaneously adjusting for behavioral covariates, and incorporated the inverse probability weighting into the models to accommodate missing data. Simulation studies showed that irrespective of whether missing by design or missing at random, the model was able to reliably determine prespecified time-varying functional forms of a given covariate coefficient, provided the the within-subject level was small. [ABSTRACT FROM AUTHOR]

In this paper, two new general families of distributions supported on the unit interval are introduced. The proposed families include several known models as special cases and define at least twenty (each one) new special models. Since the list of well-being indicators may include several double bounded random variables, the applicability for modeling those is the major practical motivation for introducing the distributions on those families. We propose a parametrization of the new families in terms of the median and develop a shiny application to provide interactive density shape illustrations for some special cases. Various properties of the introduced families are studied. Some special models in the new families are discussed. In particular, the complementary unit Weibull distribution is studied in some detail. The method of maximum likelihood for estimating the model parameters is discussed. An extensive Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples. Applications to the literacy rate in Brazilian and Colombian municipalities illustrate the usefulness of the two new families for modeling well-being indicators. [ABSTRACT FROM AUTHOR]

In this paper, we propose two multimodal circular distributions which are suitable for modeling circular data sets with two or more modes. Both distributions belong to the regular exponential family of distributions and are considered as extensions of the von Mises distribution. Hence, they possess the highly desirable properties, such as the existence of non-trivial sufficient statistics and optimal inferences for their parameters. Fine particulates (PM2.5) are generally emitted from activities such as industrial and residential combustion and from vehicle exhaust. We illustrate the utility of our proposed models using a real data set consisting of fine particulates (PM2.5) pollutant levels in Houston region during Fall season in 2019. Our results provide a strong evidence that its diurnal pattern exhibits four modes; two peaks during morning and evening rush hours and two peaks in between. [ABSTRACT FROM AUTHOR]

U.S. presidential primary debates are influential but under-researched. Before 2015, all of these debates, both Democratic and Republican, had 10 candidates or fewer. The first Republican debate in 2015, however, abided 17 candidates. They were split into two segments, with the 10 best-polling candidates in the main (prime-time) segment and the others in an 'undercard' session. A comparable pattern applied for the next six Republican debates. Concern arose not only because many candidates were crowded into a session but also because the undercard candidates were seen as receiving inferior exposure. The Democratic presidential primary debates that started four years later encountered similar difficulty. An authorized policy caused their candidates in each of the first two debates to be limited to 20, randomly divided into two groups of 10 appearing on successive nights. For remedy, this paper examines innovative debate plans, for different numbers of candidates, that feature symmetry among all candidates and entail many short segments with relatively few candidates in each. We apply combinatorial designs—balanced incomplete block designs and regular pairwise balanced designs, which are analogous to the games Spot It Jr.! Animals and (full-fledged) Spot It!, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we present a method to adjust a stochastic logistic differential equation (SLDE) to a set of highly sparse real data. We assume that the SLDE have two unknown parameters to be estimated. We calculate the Maximum Likelihood Estimator (MLE) to estimate the intrinsic growth rate. We prove that the MLE is strongly consistent and asymptotically normal. For estimating the diffusion parameter, the quadratic variation of the data is used. We validate our method with several types of simulated data. For more realistic cases in which we observe discretizations of the solution, we use diffusion bridges and the stochastic expectation-maximization algorithm to estimate the parameters. Furthermore, when we observe only one point for each path for a given number of trajectories we were still able to estimate the parameters of the SLDE. As far as we know, this is the first attempt to fit stochastic differential equations (SDEs) to these types of data. Finally, we apply our method to real data coming from fishery. The proposed adjustment method can be applied to other examples of SDEs and is highly applicable in several areas of science, especially in situations of sparse data. [ABSTRACT FROM AUTHOR]

Local Linear Regression (LLR) is a nonparametric regression model applied in the modeling phase of Response Surface Methodology (RSM). LLR does not make reference to any fixed parametric model. Hence, LLR is flexible and can capture local trends in the data that might be too complicated for the OLS. However, besides the small sample size and sparse data which characterizes RSM, the performance of the LLR model nosedives as the number of explanatory variables considered in the study increases. This phenomenon, popularly referred to as curse of dimensionality, results in the scanty application of LLR in RSM. In this paper, we propose a novel locally adaptive bandwidths selector, unlike the fixed bandwidths and existing locally adaptive bandwidths selectors, takes into account both the number of the explanatory variables in the study and their individual values at each data point. Single and multiple response problems from the literature and simulated data were used to compare the performance of the L L R P A B with those of the OLS, L L R F B and L L R A B . Neural network activation functions such ReLU, Leaky-ReLU, SELU and SPOCU was considered and give a remarkable improvement on the loss function (Mean Squared Error) over the regression models utilized in the three data. [ABSTRACT FROM AUTHOR]

In recent decades, the use of regression models with random effects has made great progress. Among these models' attractions is the flexibility to analyze correlated data. In various situations, the distribution of the response variable presents asymmetry or bimodality. In these cases, it is possible to use the normal regression with random effect at the intercept. In light of these contexts, i.e. the desire to analyze correlated data in the presence of bimodality or asymmetry, in this paper we propose a regression model with random effect at the intercept based onthe generalized inverse Gaussian distribution model with correlated data. The maximum likelihood is adopted to estimate the parameters and various simulations are performed for correlated data. A type of residuals for the new regression is proposed whose empirical distribution is close to normal. The versatility of the new regression is demonstrated by estimating the average price per hectare of bare land in 10 municipalities in the state of São Paulo (Brazil). In this context, various databases are constantly emerging, requiring flexible modeling. Thus, it is likely to be of interest to data analysts, and can make a good contribution to the statistical literature. [ABSTRACT FROM AUTHOR]

Medical clinical studies about paired body parts often involve stratified bilateral data. The correlation between responses from paired parts should be taken into account to avoid biased or misleading results. This paper aims to test if the relative risk ratios across strata are equal under the optimal algorithms. Based on different algorithms, we obtain the desired global and constrained maximum likelihood estimations (MLEs). Three asymptotic test statistics (i.e. T L , T S C and T W ) are proposed. Monte Carlo simulations are conducted to evaluate the performance of these algorithms with respect to mean square errors of MLEs and convergence rate. The empirical results show Fisher scoring algorithm is usually better than other methods since it has effective convergence rate for global MLEs, and makes mean-square error lower for constrained MLEs. Three test statistics are compared in terms of type I error rate (TIE) and power. Among these statistics, T S C is recommended according to its robust TIEs and satisfactory power. [ABSTRACT FROM AUTHOR]

Recently, the likelihood ratio (LR) test was proposed to test the homogeneity of a three-sample model with a mixture structure. Because of the presence of the mixture structure, the null limiting distribution of the LR test has a complicated supremum form, which leads to challenges in determining p-values. In addition, the LR test cannot control type-I errors well under small to moderate sample size. In this paper, we propose seven generalized fiducial methods to test the homogeneity of the three-sample model. Via simulation studies, we find that our methods perform significantly better than the LR test method in controlling the type-I errors under small to moderate sample size, while they have comparable powers in most cases. A halibut data example is used to illustrate the proposed methods. [ABSTRACT FROM AUTHOR]

Proficiency testing (PT) determines the performance of individual laboratories for specific tests or measurements and it is used to monitor the reliability of laboratories measurements. PT plays a highly valuable role as it provides objective evidence of the competence of the participant laboratories. In this paper, we propose a multivariate calibration model to assess equivalence among laboratories measurements in PT. Our method allows to deal with multivariate data, where the item under test is measured at different levels. Although intuitive, the proposed model is nonergodic, which means that the asymptotic Fisher information matrix is random. As a consequence, a detailed asymptotic analysis was carried out to establish the strategy for comparing the results of the participating laboratories. To illustrate, we apply our method to analyze the data from the Brazilian engine test group, PT program, where the power of an engine was measured by eight laboratories at several levels of rotation. [ABSTRACT FROM AUTHOR]

The clustering approach is widely accepted as the most prominent unsupervised learning problem in data mining techniques. This procedure deals with the identification of notable structures in unlabeled datasets. In modern days clustering of dynamic data, streams play a vital role in policy-making, and researchers are paying particular attention to monitoring the evolution of clustering solutions over time. The data streams evolve continually, and different sources generate data items over time. The clustering solution over this stream is not stationary and changes with the influx of new data items. This paper presents a comprehensive study of algorithms related to tracing the evolution of clusters over time in cumulative datasets. To demonstrate the applications and significance of the tracing cluster evolution, we implement the MONIC algorithm in R-software. This article illustrates how the data segmentation of dynamic streams is done and shows the applications of monitoring changes in clustering solutions with the help of real-life published datasets. [ABSTRACT FROM AUTHOR]

In this paper, a new dependent model is introduced. The model is motivated using the structure of series-parallel systems consisting of two series-parallel systems with a random number of parallel sub-systems that have fixed components connected in series. The dependence properties of the proposed model are studied. Two estimation methods, namely the moment method, and the maximum likelihood method are applied to estimate the parameters of the distributions of the components based on observing the system's lifetime data. A Monte Carlo simulation study is used to evaluate the performance of the estimators. Two real data sets are used to illustrate the proposed method. The results are useful for researchers and practitioners interested in analyzing bivariate data related to extreme events. [ABSTRACT FROM AUTHOR]

In this paper, by virtual of the inverse probability weighted technique, we considered the jump-preserving estimation on the nonparametric regression models with missing data on response variable. First, we used local piecewise-linear expansion respectively with left and right kernel to approximate the unknown regression function. Second, we obtained the left- and right-limit estimation of regression function at each observed points and then determinated the final estimators by residual sums of squares. Third, we presented the convergence rate of estimators and the residual sums of squares. Finally, we illustrated the performance of our proposed method through some simulation studies and a conjunctivitis example from The Affiliated Hospital of Hangzhou Normal University. [ABSTRACT FROM AUTHOR]

This paper presents a new method called the functional distributional clustering algorithm (FDCA) that seeks to identify spatially contiguous clusters and incorporate changes in temporal patterns across overcrowded networks. This method is motivated by a graph-based network composed of sensors arranged over space where recorded observations for each sensor represent a multi-modal distribution. The proposed method is fully non-parametric and generates clusters within an agglomerative hierarchical clustering approach based on a measure of distance that defines a cumulative distribution function over temporal changes for different locations in space. Traditional hierarchical clustering algorithms that are spatially adapted do not typically accommodate the temporal characteristics of the underlying data. The effectiveness of the FDCA is illustrated using an application to both empirical and simulated data from about 400 sensors in a 2.5 square miles network area in downtown San Francisco, California. The results demonstrate the superior ability of the the FDCA in identifying true clusters compared to functional only and distributional only algorithms and similar performance to a model-based clustering algorithm. [ABSTRACT FROM AUTHOR]

Testing the existence of quantitative trait locus (QTL) effects is an important task in QTL mapping studies. In this paper, we assume the phenotype distributions from a location-scale distribution family, and consider to test the QTL effects in both location and scale in the backcross studies with double recombination occurring. Without equal scale assumption, the log-likelihood function is unbounded, which leads to the traditional likelihood ratio test being invalid. To deal with this problem, we propose a penalized likelihood ratio test (PLRT) for testing the QTL effects. The null limiting distribution of the PLRT is shown to be a supremum of a chi-square process. As a complement, we also investigate the null limiting distribution of the likelihood ratio test for the case with equal scale assumption. The limiting distributions of the two tests under local alternatives are also studied. Simulation studies are performed to evaluate the asymptotic results and a real-data example is given for illustration. [ABSTRACT FROM AUTHOR]

In this paper, monitoring the Weibull shape parameter arising from progressively censored competing risks data is investigated. The competing risks are assumed to be independent and not identically distributed from the Weibull distributions with different shape and scale parameters. Both the shape parameters can be monitored separately by the proposed control charts using censored and predicted observations. We also introduced a control chart for monitoring both shape parameters simultaneously to detect possible shifts in both opposite and the same directions. In addition, the problem of mask data is discussed and an efficient prediction method is proposed. The behavior of the average run length with and without mask data is investigated through extensive simulations. Furthermore, the effects of sample size, number of failures due to each risk, and censoring scheme on the charts' performance are also studied. Finally, an illustrative example is presented to demonstrate the application of the proposed control charts by investigating a real data set of the failure times of two-component ARC-1 VHF communication transmitter receivers of a single commercial airline. Although this data set has been widely investigated in reliability analysis studies, this is the first time it has been analyzed in a statistical process monitoring setting. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new distribution, named unit log–log distribution, defined on the bounded (0,1) interval. Basic distributional properties such as model shapes, stochastic ordering, quantile function, moments, and order statistics of the newly defined unit distribution are studied. The maximum likelihood estimation method has been pointed out to estimate its model parameters. The new quantile regression model based on the proposed distribution is introduced and it has been derived estimations of its model parameters also. The Monte Carlo simulation studies have been given to see the performance of the estimation method based on the new unit distribution and its regression modeling. Applications of the newly defined distribution and its quantile regression model to real data sets show that the proposed models have better modeling abilities than competitive models. The proposed unit quantile regression model has targeted to explain linear relation between educational measurements of both OECD (Organization for Economic Co-operation and Development) countries and some non-members of OECD countries, and their Better Life Index. The existence of the significant covariates has been seen on the real data applications for the unit median response. [ABSTRACT FROM AUTHOR]

Discriminative subspace clustering (DSC) can make full use of linear discriminant analysis (LDA) to reduce the dimension of data and achieve effective clustering high-dimension data by clustering low-dimension data in discriminant subspace. However, most existing DSC algorithms do not consider the noise and outliers that may be contained in data sets, and when they are applied to the data sets with noise or outliers, and they often obtain poor performance due to the influence of noise and outliers. In this paper, we address the problem of the sensitivity of DSC to noise and outlier. Replacing the Euclidean distance in the objective function of LDA by an exponential non-Euclidean distance, we first develop a noise-insensitive LDA (NILDA) algorithm. Then, combining the proposed NILDA and a noise-insensitive fuzzy clustering algorithm: AFKM, we propose a noise-insensitive discriminative subspace fuzzy clustering (NIDSFC) algorithm. Experiments on some benchmark data sets show the effectiveness of the proposed NIDSFC algorithm. [ABSTRACT FROM AUTHOR]

The current large amounts of data and advanced technologies have produced new types of complex data, such as histogram-valued data. The paper focuses on classification problems when predictors are observed as or aggregated into histograms. Because conventional classification methods take vectors as input, a natural approach converts histograms into vector-valued data using summary values, such as the mean or median. However, this approach forgoes the distributional information available in histograms. To address this issue, we propose a margin-based classifier called support histogram machine (SHM) for histogram-valued data. We adopt the support vector machine framework and the Wasserstein-Kantorovich metric to measure distances between histograms. The proposed optimization problem is solved by a dual approach. We then test the proposed SHM via simulated and real examples and demonstrate its superior performance to summary-value-based methods. [ABSTRACT FROM AUTHOR]

Network (graph) data analysis is a popular research topic in statistics and machine learning. In application, one is frequently confronted with graph two-sample hypothesis testing where the goal is to test the difference between two graph populations. Several statistical tests have been devised for this purpose in the context of binary graphs. However, many of the practical networks are weighted and existing procedures cannot be directly applied to weighted graphs. In this paper, we study the weighted graph two-sample hypothesis testing problem and propose a practical test statistic. We prove that the proposed test statistic converges in distribution to the standard normal distribution under the null hypothesis and analyze its power theoretically. The simulation study shows that the proposed test has satisfactory performance and it substantially outperforms the existing counterpart in the binary graph case. A real data application is provided to illustrate the method. [ABSTRACT FROM AUTHOR]

For high-dimensional two-sample Behrens–Fisher problems, several non-scale-invariant and scale-invariant tests have been proposed. Most of them impose strong assumptions on the underlying group covariance matrices so that their test statistics are asymptotically normal. However, in practice, these assumptions may not be satisfied or hardly be checked so that these tests may not be able to maintain the nominal size well in practice. To overcome this difficulty, in this paper, a normal reference scale-invariant test is proposed and studied. It works well by neither imposing strong assumptions on the underlying group covariance matrices nor assuming their equality. It is shown that under some regularity conditions and the null hypothesis, the proposed test and a chi-square-type mixture have the same normal and non-normal limiting distributions. It is then justifiable to approximate the null distribution of the proposed test using that of the chi-square-type mixture. The distribution of the chi-square type mixture can be well approximated by the Welch–Satterthwaite chi-square-approximation with the approximation parameter consistently estimated from the data. The asymptotic power of the proposed test is established. Numerical results demonstrate that the proposed test has much better size control and power than several well-known non-scale-invariant and scale-invariant tests. [ABSTRACT FROM AUTHOR]

In this paper, we consider the estimation and model selection for longitudinal partial linear varying coefficient errors-in-variables (EV) models when the covariates are measured with some additive errors. Bias-corrected penalized quadratic inference functions method is proposed based on quadratic inference functions with two penalty function terms. The proposed method can not only handle the measurement errors of covariates and within-subject correlations but also estimate and select significant non-zero parametric and nonparametric components simultaneously. With some regularization conditions, the resulting estimators of parameters are asymptotically normal and the estimators of nonparametric varying coefficient achieves the optimal convergence rate. Furthermore, we present simulation studies and a real example analysis to evaluate the finite sample performance of the proposed method. [ABSTRACT FROM AUTHOR]

In recent days, COVID-19 pandemic has affected several people's lives globally and necessitates a massive number of screening tests to detect the existence of the coronavirus. At the same time, the rise of deep learning (DL) concepts helps to effectively develop a COVID-19 diagnosis model to attain maximum detection rate with minimum computation time. This paper presents a new Residual Network (ResNet) based Class Attention Layer with Bidirectional LSTM called RCAL-BiLSTM for COVID-19 Diagnosis. The proposed RCAL-BiLSTM model involves a series of processes namely bilateral filtering (BF) based preprocessing, RCAL-BiLSTM based feature extraction, and softmax (SM) based classification. Once the BF technique produces the preprocessed image, RCAL-BiLSTM based feature extraction process takes place using three modules, namely ResNet based feature extraction, CAL, and Bi-LSTM modules. Finally, the SM layer is applied to categorize the feature vectors into corresponding feature maps. The experimental validation of the presented RCAL-BiLSTM model is tested against Chest-X-Ray dataset and the results are determined under several aspects. The experimental outcome pointed out the superior nature of the RCAL-BiLSTM model by attaining maximum sensitivity of 93.28%, specificity of 94.61%, precision of 94.90%, accuracy of 94.88%, F-score of 93.10% and kappa value of 91.40%. [ABSTRACT FROM AUTHOR]

The extreme value distribution was developed for modeling extreme-order statistics or extreme events. In this study, we discuss the distribution of the largest extreme. The main objective of this paper is to determine the best estimators of the unknown parameters of the extreme value distribution. Thus, both classical and Bayesian methods are used. The classical estimation methods under consideration are maximum likelihood estimators, moment's estimators, least squares estimators, and weighted least squares estimators, percentile estimators, the ordinary least squares estimators, best linear unbiased estimators, L-moments estimators, trimmed L-moments estimators, and Bain and Engelhardt estimators. We also propose new estimators for the unknown parameters. Bayesian estimators of the parameters are derived by using Lindley's approximation and Markov Chain Monte Carlo methods. The asymptotic confidence intervals are considered by using maximum likelihood estimators. The Bayesian credible intervals are also obtained by using Gibbs sampling. The performances of these estimation methods are compared with respect to their biases and mean square errors through a simulation study. The maximum daily flood discharge (annual) data sets of the Meriç River and Feather River are analyzed at the end of the study for a better understanding of the methods presented in this paper. [ABSTRACT FROM AUTHOR]

In this paper, we present a testing procedure for fractional orders of integration in the context of non-linear terms approximated by Fourier functions. The test statistic has an asymptotic standard normal distribution and several Monte Carlo experiments conducted in the paper show that it performs well in finite samples. Various applications using real life time series, such as US unemployment rates, US GNP and Purchasing Power Parity (PPP) of G7 countries are presented at the end of the paper. [ABSTRACT FROM AUTHOR]

Measures of association play a central role in the social sciences to quantify the strength of a linear relationship between the variables of interest. In many applications researchers can translate scientific expectations to hypotheses with equality and/or order constraints on these measures of association. In this paper a Bayes factor test is proposed for testing multiple hypotheses with constraints on the measures of association between ordinal and/or continuous variables, possibly after correcting for certain covariates. This test can be used to obtain a direct answer to the research question how much evidence there is in the data for a social science theory relative to competing theories. The stand-alone software package 'BCT' allows users to apply the methodology in an easy manner. The methodology will also be available in the R package 'BFpack'. An empirical application from leisure studies about the associations between life, leisure and relationship satisfaction and an application about the differences about egalitarian justice beliefs across countries are used to illustrate the methodology. [ABSTRACT FROM AUTHOR]