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A Bayesian approach for clustering and exact finite-sample model selection in longitudinal data mixtures 在纵向数据混合物中进行聚类和精确有限样本模型选择的贝叶斯方法
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-05-08 DOI: 10.1007/s00180-024-01501-5
M. Corneli, E. Erosheva, X. Qian, M. Lorenzi
{"title":"A Bayesian approach for clustering and exact finite-sample model selection in longitudinal data mixtures","authors":"M. Corneli, E. Erosheva, X. Qian, M. Lorenzi","doi":"10.1007/s00180-024-01501-5","DOIUrl":"https://doi.org/10.1007/s00180-024-01501-5","url":null,"abstract":"<p>We consider mixtures of longitudinal trajectories, where one trajectory contains measurements over time of the variable of interest for one individual and each individual belongs to one cluster. The number of clusters as well as individual cluster memberships are unknown and must be inferred. We propose an original Bayesian clustering framework that allows us to obtain an exact finite-sample model selection criterion for selecting the number of clusters. Our finite-sample approach is more flexible and parsimonious than asymptotic alternatives such as Bayesian information criterion or integrated classification likelihood criterion in the choice of the number of clusters. Moreover, our approach has other desirable qualities: (i) it keeps the computational effort of the clustering algorithm under control and (ii) it generalizes to several families of regression mixture models, from linear to purely non-parametric. We test our method on simulated datasets as well as on a real world dataset from the Alzheimer’s disease neuroimaging initative database.</p>","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Mixture models for simultaneous classification and reduction of three-way data 用于同时分类和还原三向数据的混合模型
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-05-06 DOI: 10.1007/s00180-024-01478-1
Roberto Rocci, Maurizio Vichi, Monia Ranalli
{"title":"Mixture models for simultaneous classification and reduction of three-way data","authors":"Roberto Rocci, Maurizio Vichi, Monia Ranalli","doi":"10.1007/s00180-024-01478-1","DOIUrl":"https://doi.org/10.1007/s00180-024-01478-1","url":null,"abstract":"<p>Finite mixture of Gaussians are often used to classify two- (units and variables) or three- (units, variables and occasions) way data. However, two issues arise: model complexity and capturing the true cluster structure. Indeed, a large number of variables and/or occasions implies a large number of model parameters; while the existence of noise variables (and/or occasions) could mask the true cluster structure. The approach adopted in the present paper is to reduce the number of model parameters by identifying a sub-space containing the information needed to classify the observations. This should also help in identifying noise variables and/or occasions. The maximum likelihood model estimation is carried out through an EM-like algorithm. The effectiveness of the proposal is assessed through a simulation study and an application to real data.</p>","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Robust Bayesian cumulative probit linear mixed models for longitudinal ordinal data 用于纵向序数数据的稳健贝叶斯累积概率线性混合模型
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-05-04 DOI: 10.1007/s00180-024-01499-w
Kuo-Jung Lee, Ray-Bing Chen, Keunbaik Lee
{"title":"Robust Bayesian cumulative probit linear mixed models for longitudinal ordinal data","authors":"Kuo-Jung Lee, Ray-Bing Chen, Keunbaik Lee","doi":"10.1007/s00180-024-01499-w","DOIUrl":"https://doi.org/10.1007/s00180-024-01499-w","url":null,"abstract":"<p>Longitudinal studies have been conducted in various fields, including medicine, economics and the social sciences. In this paper, we focus on longitudinal ordinal data. Since the longitudinal data are collected over time, repeated outcomes within each subject may be serially correlated. To address both the within-subjects serial correlation and the specific variance between subjects, we propose a Bayesian cumulative probit random effects model for the analysis of longitudinal ordinal data. The hypersphere decomposition approach is employed to overcome the positive definiteness constraint and high-dimensionality of the correlation matrix. Additionally, we present a hybrid Gibbs/Metropolis-Hastings algorithm to efficiently generate cutoff points from truncated normal distributions, thereby expediting the convergence of the Markov Chain Monte Carlo (MCMC) algorithm. The performance and robustness of our proposed methodology under misspecified correlation matrices are demonstrated through simulation studies under complete data, missing completely at random (MCAR), and missing at random (MAR). We apply the proposed approach to analyze two sets of actual ordinal data: the arthritis dataset and the lung cancer dataset. To facilitate the implementation of our method, we have developed <span>BayesRGMM</span>, an open-source R package available on CRAN, accompanied by comprehensive documentation and source code accessible at https://github.com/kuojunglee/BayesRGMM/.</p>","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
R-estimation in linear models: algorithms, complexity, challenges 线性模型中的 R 估计：算法、复杂性和挑战
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-05-03 DOI: 10.1007/s00180-024-01495-0
Jaromír Antoch, Michal Černý, Ryozo Miura
{"title":"R-estimation in linear models: algorithms, complexity, challenges","authors":"Jaromír Antoch, Michal Černý, Ryozo Miura","doi":"10.1007/s00180-024-01495-0","DOIUrl":"https://doi.org/10.1007/s00180-024-01495-0","url":null,"abstract":"<p>The main objective of this paper is to discuss selected computational aspects of robust estimation in the linear model with the emphasis on <i>R</i>-estimators. We focus on numerical algorithms and computational efficiency rather than on statistical properties. In addition, we formulate some algorithmic properties that a “good” method for <i>R</i>-estimators is expected to satisfy and show how to satisfy them using the currently available algorithms. We illustrate both good and bad properties of the existing algorithms. We propose two-stage methods to minimize the effect of the bad properties. Finally we justify a challenge for new approaches based on interior-point methods in optimization.</p>","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140889694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Two-stage regression spline modeling based on local polynomial kernel regression 基于局部多项式核回归的两阶段回归样条线建模
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-05-01 DOI: 10.1007/s00180-024-01498-x
Hamid Mraoui, Ahmed El-Alaoui, Souad Bechrouri, Nezha Mohaoui, Abdelilah Monir
{"title":"Two-stage regression spline modeling based on local polynomial kernel regression","authors":"Hamid Mraoui, Ahmed El-Alaoui, Souad Bechrouri, Nezha Mohaoui, Abdelilah Monir","doi":"10.1007/s00180-024-01498-x","DOIUrl":"https://doi.org/10.1007/s00180-024-01498-x","url":null,"abstract":"<p>This paper introduces a new nonparametric estimator of the regression based on local quasi-interpolation spline method. This model combines a B-spline basis with a simple local polynomial regression, via blossoming approach, to produce a reduced rank spline like smoother. Different coefficients functionals are allowed to have different smoothing parameters (bandwidths) if the function has different smoothness. In addition, the number and location of the knots of this estimator are not fixed. In practice, we may employ a modest number of basis functions and then determine the smoothing parameter as the minimizer of the criterion. In simulations, the approach achieves very competitive performance with P-spline and smoothing spline methods. Simulated data and a real data example are used to illustrate the effectiveness of the method proposed in this paper.</p>","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140827212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Advancements in reliability estimation for the exponentiated Pareto distribution: a comparison of classical and Bayesian methods with lower record values 指数化帕累托分布可靠性估计的进展：使用较低记录值的经典方法与贝叶斯方法的比较
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-04-29 DOI: 10.1007/s00180-024-01497-y
Shubham Saini
{"title":"Advancements in reliability estimation for the exponentiated Pareto distribution: a comparison of classical and Bayesian methods with lower record values","authors":"Shubham Saini","doi":"10.1007/s00180-024-01497-y","DOIUrl":"https://doi.org/10.1007/s00180-024-01497-y","url":null,"abstract":"<p>Estimating the reliability of multicomponent systems is crucial in various engineering and reliability analysis applications. This paper investigates the multicomponent stress strength reliability estimation using lower record values, specifically for the exponentiated Pareto distribution. We compare classical estimation techniques, such as maximum likelihood estimation, with Bayesian estimation methods. Under Bayesian estimation, we employ Markov Chain Monte Carlo techniques and Tierney–Kadane’s approximation to obtain the posterior distribution of the reliability parameter. To evaluate the performance of the proposed estimation approaches, we conduct a comprehensive simulation study, considering various system configurations and sample sizes. Additionally, we analyze real data to illustrate the practical applicability of our methods. The proposed methodologies provide valuable insights for engineers and reliability analysts in accurately assessing the reliability of multicomponent systems using lower record values.</p>","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
PDAS: a Newton-type method for $$L_0$$ regularized accelerated failure time model PDAS：用于$L_0$$正则化加速失效时间模型的牛顿型方法
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-04-21 DOI: 10.1007/s00180-024-01496-z
Ning Su, Yanyan Liu, Lican Kang
{"title":"PDAS: a Newton-type method for $$L_0$$ regularized accelerated failure time model","authors":"Ning Su, Yanyan Liu, Lican Kang","doi":"10.1007/s00180-024-01496-z","DOIUrl":"https://doi.org/10.1007/s00180-024-01496-z","url":null,"abstract":"","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140678458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Tips and tricks for Bayesian VAR models in gretl 在 gretl 中使用贝叶斯 VAR 模型的技巧和窍门
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-04-20 DOI: 10.1007/s00180-024-01492-3
Luca Pedini
{"title":"Tips and tricks for Bayesian VAR models in gretl","authors":"Luca Pedini","doi":"10.1007/s00180-024-01492-3","DOIUrl":"https://doi.org/10.1007/s00180-024-01492-3","url":null,"abstract":"","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140679518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Penalized function-on-function linear quantile regression 受惩罚的函数对函数线性量化回归
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-04-17 DOI: 10.1007/s00180-024-01494-1
U. Beyaztas, Han Lin Shang, Semanur Saricam
{"title":"Penalized function-on-function linear quantile regression","authors":"U. Beyaztas, Han Lin Shang, Semanur Saricam","doi":"10.1007/s00180-024-01494-1","DOIUrl":"https://doi.org/10.1007/s00180-024-01494-1","url":null,"abstract":"","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140690988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Network and attribute-based clustering of tennis players and tournaments 基于网络和属性的网球运动员和赛事聚类
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-04-16 DOI: 10.1007/s00180-024-01493-2
P. D’Urso, Livia De Giovanni, Lorenzo Federico, V. Vitale
{"title":"Network and attribute-based clustering of tennis players and tournaments","authors":"P. D’Urso, Livia De Giovanni, Lorenzo Federico, V. Vitale","doi":"10.1007/s00180-024-01493-2","DOIUrl":"https://doi.org/10.1007/s00180-024-01493-2","url":null,"abstract":"","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140696315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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