Pub Date : 2026-01-01Epub Date: 2025-08-06DOI: 10.1016/j.csda.2025.108255
Uche Mbaka , James Owen Ramsay , Michelle Carey
Functional data analysis frequently involves estimating a smooth covariance function based on observed data. This estimation is essential for understanding interactions among functions and constitutes a fundamental aspect of numerous advanced methodologies, including functional principal component analysis. Two approaches for estimating smooth covariance functions in the presence of measurement errors are introduced. The first method employs a low-rank approximation of the covariance matrix, while the second ensures positive definiteness via a Cholesky decomposition. Both approaches employ the use of penalized regression to produce smooth covariance estimates and have been validated through comprehensive simulation studies. The practical application of these methods is demonstrated through the examination of average weekly milk yields in dairy cows as well as egg-laying patterns of Mediterranean fruit flies.
{"title":"Estimating a smooth covariance for functional data","authors":"Uche Mbaka , James Owen Ramsay , Michelle Carey","doi":"10.1016/j.csda.2025.108255","DOIUrl":"10.1016/j.csda.2025.108255","url":null,"abstract":"<div><div>Functional data analysis frequently involves estimating a smooth covariance function based on observed data. This estimation is essential for understanding interactions among functions and constitutes a fundamental aspect of numerous advanced methodologies, including functional principal component analysis. Two approaches for estimating smooth covariance functions in the presence of measurement errors are introduced. The first method employs a low-rank approximation of the covariance matrix, while the second ensures positive definiteness via a Cholesky decomposition. Both approaches employ the use of penalized regression to produce smooth covariance estimates and have been validated through comprehensive simulation studies. The practical application of these methods is demonstrated through the examination of average weekly milk yields in dairy cows as well as egg-laying patterns of Mediterranean fruit flies.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"213 ","pages":"Article 108255"},"PeriodicalIF":1.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-07-29DOI: 10.1016/j.csda.2025.108254
Quan Vu , Francis K.C. Hui , Samuel Muller , A.H. Welsh
When fitting generalized linear mixed models, choosing the random effects distribution is an important decision. As random effects are unobserved, misspecification of their distribution is a real possibility. Thus, the consequences of random effects misspecification for point prediction and prediction inference of random effects in generalized linear mixed models need to be investigated. A combination of theory, simulation, and a real application is used to explore the effect of using the common normality assumption for the random effects distribution when the correct specification is a mixture of normal distributions, focusing on the impacts on point prediction, mean squared prediction errors, and prediction intervals. Results show that the level of shrinkage for the predicted random effects can differ greatly under the two random effect distributions, and so is susceptible to misspecification. Also, the unconditional mean squared prediction errors for the random effects are almost always larger under the misspecified normal random effects distribution, while results for the mean squared prediction errors conditional on the random effects are more complicated but remain generally larger under the misspecified distribution (especially when the true random effect is close to the mean of one of the component distributions in the true mixture distribution). Results for prediction intervals indicate that the overall coverage probability is, in contrast, not greatly impacted by misspecification. It is concluded that misspecifying the random effects distribution can affect prediction of random effects, and greater caution is recommended when adopting the normality assumption in generalized linear mixed models.
{"title":"Random effects misspecification and its consequences for prediction in generalized linear mixed models","authors":"Quan Vu , Francis K.C. Hui , Samuel Muller , A.H. Welsh","doi":"10.1016/j.csda.2025.108254","DOIUrl":"10.1016/j.csda.2025.108254","url":null,"abstract":"<div><div>When fitting generalized linear mixed models, choosing the random effects distribution is an important decision. As random effects are unobserved, misspecification of their distribution is a real possibility. Thus, the consequences of random effects misspecification for point prediction and prediction inference of random effects in generalized linear mixed models need to be investigated. A combination of theory, simulation, and a real application is used to explore the effect of using the common normality assumption for the random effects distribution when the correct specification is a mixture of normal distributions, focusing on the impacts on point prediction, mean squared prediction errors, and prediction intervals. Results show that the level of shrinkage for the predicted random effects can differ greatly under the two random effect distributions, and so is susceptible to misspecification. Also, the unconditional mean squared prediction errors for the random effects are almost always larger under the misspecified normal random effects distribution, while results for the mean squared prediction errors conditional on the random effects are more complicated but remain generally larger under the misspecified distribution (especially when the true random effect is close to the mean of one of the component distributions in the true mixture distribution). Results for prediction intervals indicate that the overall coverage probability is, in contrast, not greatly impacted by misspecification. It is concluded that misspecifying the random effects distribution can affect prediction of random effects, and greater caution is recommended when adopting the normality assumption in generalized linear mixed models.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"213 ","pages":"Article 108254"},"PeriodicalIF":1.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-08-13DOI: 10.1016/j.csda.2025.108258
Yichen Cheng , Yichuan Zhao
Empirical likelihood is a popular nonparametric statistical tool that does not require any distributional assumptions. The possibility of conducting variable selection via Bayesian empirical likelihood is studied both theoretically and empirically. Theoretically, it is shown that when the prior distribution satisfies certain mild conditions, the corresponding Bayesian empirical likelihood estimators are posteriorly consistent and variable selection consistent. As special cases, the prior of Bayesian empirical likelihood LASSO and SCAD satisfy such conditions and thus can identify the non-zero elements of the parameters with probability approaching 1. In addition, it is easy to verify that those conditions are met for other widely used priors such as ridge, elastic net and adaptive LASSO. Empirical likelihood depends on a parameter that needs to be obtained by numerically solving a non-linear equation. Thus, there exists no conjugate prior for the posterior distribution, which causes the slow convergence of the MCMC sampling algorithm in some cases. To solve this problem, an approximation distribution is used as the proposal to enhance the acceptance rate and, therefore, facilitate faster computation. The computational results demonstrate quick convergence for the examples used in the paper. Both simulations and real data analyses are performed to illustrate the advantages of the proposed methods.
{"title":"Empirical likelihood based Bayesian variable selection","authors":"Yichen Cheng , Yichuan Zhao","doi":"10.1016/j.csda.2025.108258","DOIUrl":"10.1016/j.csda.2025.108258","url":null,"abstract":"<div><div>Empirical likelihood is a popular nonparametric statistical tool that does not require any distributional assumptions. The possibility of conducting variable selection via Bayesian empirical likelihood is studied both theoretically and empirically. Theoretically, it is shown that when the prior distribution satisfies certain mild conditions, the corresponding Bayesian empirical likelihood estimators are posteriorly consistent and variable selection consistent. As special cases, the prior of Bayesian empirical likelihood LASSO and SCAD satisfy such conditions and thus can identify the non-zero elements of the parameters with probability approaching 1. In addition, it is easy to verify that those conditions are met for other widely used priors such as ridge, elastic net and adaptive LASSO. Empirical likelihood depends on a parameter that needs to be obtained by numerically solving a non-linear equation. Thus, there exists no conjugate prior for the posterior distribution, which causes the slow convergence of the MCMC sampling algorithm in some cases. To solve this problem, an approximation distribution is used as the proposal to enhance the acceptance rate and, therefore, facilitate faster computation. The computational results demonstrate quick convergence for the examples used in the paper. Both simulations and real data analyses are performed to illustrate the advantages of the proposed methods.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"213 ","pages":"Article 108258"},"PeriodicalIF":1.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-07-22DOI: 10.1016/j.csda.2025.108251
Matteo Framba , Veronica Vinciotti , Ernst C. Wit
Parameter estimation of kinetic rates in stochastic quasi-reaction systems can be challenging, particularly when the time gap between consecutive measurements is large. Local linear approximation approaches account for the stochasticity in the system but fail to capture the intrinsically nonlinear nature of the mean dynamics of the process. Moreover, the mean dynamics of a quasi-reaction system can be described by a system of ODEs, which have an explicit solution only for simple unitary systems. An approximate analytical solution is derived for generic quasi-reaction systems via a first-order Taylor approximation of the hazard rate. This allows a nonlinear forward prediction of the future dynamics given the current state of the system. Predictions and corresponding observations are embedded in a nonlinear least-squares approach for parameter estimation. The performance of the algorithm is compared to existing methods via a simulation study. Besides the generality of the approach in the specification of the quasi-reaction system and the gains in computational efficiency, the results show an improvement in the kinetic rate estimation, particularly for data observed at large time intervals. Additionally, the availability of an explicit solution makes the method robust to stiffness, which is often present in biological systems. Application to Rhesus Macaque data illustrates the use of the method in the study of cell differentiation.
{"title":"Inferring the dynamics of quasi-reaction systems via nonlinear local mean-field approximations","authors":"Matteo Framba , Veronica Vinciotti , Ernst C. Wit","doi":"10.1016/j.csda.2025.108251","DOIUrl":"10.1016/j.csda.2025.108251","url":null,"abstract":"<div><div>Parameter estimation of kinetic rates in stochastic quasi-reaction systems can be challenging, particularly when the time gap between consecutive measurements is large. Local linear approximation approaches account for the stochasticity in the system but fail to capture the intrinsically nonlinear nature of the mean dynamics of the process. Moreover, the mean dynamics of a quasi-reaction system can be described by a system of ODEs, which have an explicit solution only for simple unitary systems. An approximate analytical solution is derived for generic quasi-reaction systems via a first-order Taylor approximation of the hazard rate. This allows a nonlinear forward prediction of the future dynamics given the current state of the system. Predictions and corresponding observations are embedded in a nonlinear least-squares approach for parameter estimation. The performance of the algorithm is compared to existing methods via a simulation study. Besides the generality of the approach in the specification of the quasi-reaction system and the gains in computational efficiency, the results show an improvement in the kinetic rate estimation, particularly for data observed at large time intervals. Additionally, the availability of an explicit solution makes the method robust to stiffness, which is often present in biological systems. Application to Rhesus Macaque data illustrates the use of the method in the study of cell differentiation.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"213 ","pages":"Article 108251"},"PeriodicalIF":1.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-07-16DOI: 10.1016/j.csda.2025.108248
Arthur Pewsey
The cardioid distribution, despite being one of the fundamental models for circular data, has received limited attention both methodologically and in terms of its implementation in R. To redress these shortcomings, published results on the model are summarized, corrected and extended, and the scope and limitations of the existing support for the model in R identified. A thorough investigation into the performance of trigonometric moment and maximum likelihood based approaches to point and interval estimation of the model's location and concentration parameters is presented, and goodness-of-fit techniques outlined. A suite of reliable R functions is provided for the model's practical application. The application of the proposed inferential methods and R functions is illustrated by an analysis of palaeocurrent cross-bed azimuths.
{"title":"On Jeffreys's cardioid distribution","authors":"Arthur Pewsey","doi":"10.1016/j.csda.2025.108248","DOIUrl":"10.1016/j.csda.2025.108248","url":null,"abstract":"<div><div>The cardioid distribution, despite being one of the fundamental models for circular data, has received limited attention both methodologically and in terms of its implementation in R. To redress these shortcomings, published results on the model are summarized, corrected and extended, and the scope and limitations of the existing support for the model in R identified. A thorough investigation into the performance of trigonometric moment and maximum likelihood based approaches to point and interval estimation of the model's location and concentration parameters is presented, and goodness-of-fit techniques outlined. A suite of reliable R functions is provided for the model's practical application. The application of the proposed inferential methods and R functions is illustrated by an analysis of palaeocurrent cross-bed azimuths.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"213 ","pages":"Article 108248"},"PeriodicalIF":1.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-08-05DOI: 10.1016/j.csda.2025.108256
Hyungwoo Kim , Seung Jun Shin
Optimizing the receiver operating characteristic (ROC) curve is a popular way to evaluate a binary classifier under imbalanced scenarios frequently encountered in practice. A practical approach to constructing a linear binary classifier is presented by simultaneously optimizing the area under the ROC curve (AUC) and selecting informative variables in high dimensions. In particular, the smoothly clipped absolute deviation (SCAD) penalty is employed, and its oracle property is established, which enables the development of a consistent BIC-type information criterion that greatly facilitates the tuning procedure. Both simulated and real data analyses demonstrate the promising performance of the proposed method in terms of AUC optimization and variable selection.
{"title":"Variable selection in AUC-optimizing classification","authors":"Hyungwoo Kim , Seung Jun Shin","doi":"10.1016/j.csda.2025.108256","DOIUrl":"10.1016/j.csda.2025.108256","url":null,"abstract":"<div><div>Optimizing the receiver operating characteristic (ROC) curve is a popular way to evaluate a binary classifier under imbalanced scenarios frequently encountered in practice. A practical approach to constructing a linear binary classifier is presented by simultaneously optimizing the area under the ROC curve (AUC) and selecting informative variables in high dimensions. In particular, the smoothly clipped absolute deviation (SCAD) penalty is employed, and its oracle property is established, which enables the development of a consistent BIC-type information criterion that greatly facilitates the tuning procedure. Both simulated and real data analyses demonstrate the promising performance of the proposed method in terms of AUC optimization and variable selection.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"213 ","pages":"Article 108256"},"PeriodicalIF":1.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144828904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Spatio-temporal point pattern data are becoming prevalent in many scientific disciplines. We consider a sequence of spatial point processes where each point process is Poisson given the past. We model the conditional first-order intensity function of each point process as a parametric log-linear function of spatial, temporal, and spatio-temporal covariates that may depend on previous point patterns. Dealing with spatio-temporal covariates brings computational and methodological challenges compared to the purely spatial case. We extend regularisation methods for spatial point process variable selection to obtain parsimonious and interpretable models in the considered spatio-temporal case. Using our proposed methodology, we conduct two simulation studies and examine an application to criminal activity in the Kennedy district of Bogota. In the application, we consider a spatio-temporal point pattern data of crime locations and a number of spatial, temporal, and spatio-temporal covariates related to urban places, environmental factors, and further space-time factors. The intensity function of vehicle thefts is estimated, considering other crimes as covariate information. The proposed methodology offers a comprehensive approach for analysing spatio-temporal point pattern crime data, capturing complex relationships between covariates and crime occurrences over space and time.
{"title":"Variable selection for spatio-temporal conditionally Poisson point processes","authors":"Achmad Choiruddin , Jonatan A. González , Jorge Mateu , Alwan Fadlurohman , Rasmus Waagepetersen","doi":"10.1016/j.csda.2025.108238","DOIUrl":"10.1016/j.csda.2025.108238","url":null,"abstract":"<div><div>Spatio-temporal point pattern data are becoming prevalent in many scientific disciplines. We consider a sequence of spatial point processes where each point process is Poisson given the past. We model the conditional first-order intensity function of each point process as a parametric log-linear function of spatial, temporal, and spatio-temporal covariates that may depend on previous point patterns. Dealing with spatio-temporal covariates brings computational and methodological challenges compared to the purely spatial case. We extend regularisation methods for spatial point process variable selection to obtain parsimonious and interpretable models in the considered spatio-temporal case. Using our proposed methodology, we conduct two simulation studies and examine an application to criminal activity in the Kennedy district of Bogota. In the application, we consider a spatio-temporal point pattern data of crime locations and a number of spatial, temporal, and spatio-temporal covariates related to urban places, environmental factors, and further space-time factors. The intensity function of vehicle thefts is estimated, considering other crimes as covariate information. The proposed methodology offers a comprehensive approach for analysing spatio-temporal point pattern crime data, capturing complex relationships between covariates and crime occurrences over space and time.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"212 ","pages":"Article 108238"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-07-05DOI: 10.1016/j.csda.2025.108242
Xiaoge Xiong
Two novel test procedures are proposed for the identity and sphericity of covariance matrices in high-dimensional asymptotic frameworks, both constructed via U-statistics. The limiting distributions of these tests are established under null and local alternative hypotheses. Monte Carlo simulation results demonstrate their superiority over several competing methods across various scenarios, with the proposed tests achieving full power against both dense and sparse alternatives. The effectiveness of the proposed tests is further validated through an application to a colon dataset.
{"title":"New tests for the identity and sphericity of high-dimensional covariance matrices via U-statistics","authors":"Xiaoge Xiong","doi":"10.1016/j.csda.2025.108242","DOIUrl":"10.1016/j.csda.2025.108242","url":null,"abstract":"<div><div>Two novel test procedures are proposed for the identity and sphericity of covariance matrices in high-dimensional asymptotic frameworks, both constructed via U-statistics. The limiting distributions of these tests are established under null and local alternative hypotheses. Monte Carlo simulation results demonstrate their superiority over several competing methods across various scenarios, with the proposed tests achieving full power against both dense and sparse alternatives. The effectiveness of the proposed tests is further validated through an application to a colon dataset.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"212 ","pages":"Article 108242"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-07-07DOI: 10.1016/j.csda.2025.108239
Lu Wang , Xiang Lyu , Lexin Li
There is increasing interest in modeling high-dimensional longitudinal outcomes in applications such as developmental neuroimaging research. Growth curve model offers a useful tool to capture both the mean growth pattern across individuals, as well as the dynamic changes of outcomes over time within each individual. However, when the number of outcomes is large, it becomes challenging and often infeasible to tackle the large covariance matrix of the random effects involved in the model. A high-dimensional response growth curve model, with three novel components, is proposed: a low-rank factor model structure that substantially reduces the number of parameters in the large covariance matrix, a re-parameterization formulation coupled with a sparsity penalty that selects important fixed and random effect terms, and a computational trick that turns the inversion of a large matrix into the inversion of a stack of small matrices and thus considerably speeds up the computation. An efficient expectation-maximization-type estimation algorithm is developed, and the competitive performance of the proposed method is demonstrated through both simulations and a longitudinal study of brain structural connectivity in association with human immunodeficiency virus.
{"title":"High-dimensional response growth curve modeling for longitudinal neuroimaging analysis","authors":"Lu Wang , Xiang Lyu , Lexin Li","doi":"10.1016/j.csda.2025.108239","DOIUrl":"10.1016/j.csda.2025.108239","url":null,"abstract":"<div><div>There is increasing interest in modeling high-dimensional longitudinal outcomes in applications such as developmental neuroimaging research. Growth curve model offers a useful tool to capture both the mean growth pattern across individuals, as well as the dynamic changes of outcomes over time within each individual. However, when the number of outcomes is large, it becomes challenging and often infeasible to tackle the large covariance matrix of the random effects involved in the model. A high-dimensional response growth curve model, with three novel components, is proposed: a low-rank factor model structure that substantially reduces the number of parameters in the large covariance matrix, a re-parameterization formulation coupled with a sparsity penalty that selects important fixed and random effect terms, and a computational trick that turns the inversion of a large matrix into the inversion of a stack of small matrices and thus considerably speeds up the computation. An efficient expectation-maximization-type estimation algorithm is developed, and the competitive performance of the proposed method is demonstrated through both simulations and a longitudinal study of brain structural connectivity in association with human immunodeficiency virus.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"212 ","pages":"Article 108239"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-07-04DOI: 10.1016/j.csda.2025.108243
Nuo Xu, Kai Yang
The modeling of high-dimensional time series has always been an appealing and challenging problem. The main difficulties of modeling high-dimensional time series lie in the curse of dimensionality and complex cross dependence between adjacent components. To solve these problems for high-dimensional time series of counts, a class of high-dimensional and banded integer-valued autoregressive processes without assuming the innovation's distribution is proposed. A banded thinning structure is constructed to diminish the parameters' dimension. The componentwise conditional least squares and weighted conditional least squares methods are developed to estimate the banded autoregressive coefficient matrices. The bandwidth parameter is identified via a marginal Bayesian information criterion method. Some numerical results are provided to show the good performance of the estimators. Finally, the superiority of the proposed model is shown by an application to an air quality data set of different cities.
{"title":"High-dimensional and banded integer-valued autoregressive processes","authors":"Nuo Xu, Kai Yang","doi":"10.1016/j.csda.2025.108243","DOIUrl":"10.1016/j.csda.2025.108243","url":null,"abstract":"<div><div>The modeling of high-dimensional time series has always been an appealing and challenging problem. The main difficulties of modeling high-dimensional time series lie in the curse of dimensionality and complex cross dependence between adjacent components. To solve these problems for high-dimensional time series of counts, a class of high-dimensional and banded integer-valued autoregressive processes without assuming the innovation's distribution is proposed. A banded thinning structure is constructed to diminish the parameters' dimension. The componentwise conditional least squares and weighted conditional least squares methods are developed to estimate the banded autoregressive coefficient matrices. The bandwidth parameter is identified via a marginal Bayesian information criterion method. Some numerical results are provided to show the good performance of the estimators. Finally, the superiority of the proposed model is shown by an application to an air quality data set of different cities.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"212 ","pages":"Article 108243"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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