Computational Statistics & Data Analysis最新文献_第5页

GMM estimation and variable selection of semiparametric model with increasing dimension and high-order spatial dependence 高维高阶空间依赖半参数模型的GMM估计与变量选择

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-12-30 DOI: 10.1016/j.csda.2024.108113

Fang Lu , Hao Pan , Jing Yang

{"title":"GMM estimation and variable selection of semiparametric model with increasing dimension and high-order spatial dependence","authors":"Fang Lu , Hao Pan , Jing Yang","doi":"10.1016/j.csda.2024.108113","DOIUrl":"10.1016/j.csda.2024.108113","url":null,"abstract":"<div><div>To address various forms of spatial dependence and the heterogeneous effects of the impacts of some regressors, this paper concentrates on the generalized method of moments (GMM) estimation and variable selection of higher-order spatial autoregressive (SAR) model with semi-varying coefficients and diverging number of parameters. With the varying coefficient functions being approximated by basis functions, the GMM estimation procedure is firstly proposed and then, a novel and convenient smooth-threshold GMM procedure is constructed for variable selection based on the smooth-threshold estimating equations. Under some regularity conditions, the asymptotic properties of the proposed estimation and variable selection methods are established. In particular, the asymptotic normality of the parametric estimator is derived via a novel way based on some fundamental operations on block matrix. Compared to the existing estimation methods of semiparametric SAR models, our proposed series-based GMM procedure can simultaneously enjoy the merits of lower computing cost, higher estimation accuracy or higher applicability, especially in the case of heteroscedasticity. Extensive numerical simulations are conducted to confirm the theories and to demonstrate the advantages of the proposed method, in finite sample performance. Two real data analysis are further followed for application.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"205 ","pages":"Article 108113"},"PeriodicalIF":1.5,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161771","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}

引用次数: 0

A debiasing phylogenetic tree-assisted regression model for microbiome data 微生物组数据的去偏系统发育树辅助回归模型

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-12-19 DOI: 10.1016/j.csda.2024.108111

Yanhui Li , Luqing Zhao , Jinjuan Wang

{"title":"A debiasing phylogenetic tree-assisted regression model for microbiome data","authors":"Yanhui Li , Luqing Zhao , Jinjuan Wang","doi":"10.1016/j.csda.2024.108111","DOIUrl":"10.1016/j.csda.2024.108111","url":null,"abstract":"<div><div>Identifying associations between microbial taxa and sample features has always been a worthwhile issue in microbiome analysis and various regression-based methods have been proposed. These methods can roughly be divided into two types. One considers sparsity characteristic of the microbiome data in the analysis, and the other considers phylogenetic tree to employ evolutionary information. However, none of these methods apply both sparsity and phylogenetic tree thoroughly in the regression analysis with theoretical guarantees. To fill this gap, a phylogenetic tree-assisted regression model accompanied by a Lasso-type penalty is proposed to detect feature-related microbial compositions. Specifically, based on the rational assumption that the smaller the phylogenetic distance between two microbial species, the closer their coefficients in the regression model, the phylogenetic tree is accommodated into the regression model by constructing a Laplacian-type penalty in the loss function. Both linear regression model for continuous outcome and generalized linear regression model for categorical outcome are analyzed in this framework. Additionally, debiasing algorithms are proposed for the coefficient estimators to give more precise evaluation. Extensive numerical simulations and real data analyses demonstrate the higher efficiency of the proposed method.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"205 ","pages":"Article 108111"},"PeriodicalIF":1.5,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161772","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}

引用次数: 0

On summed nonparametric dependence measures in high dimensions, fixed or large samples 对高维、固定或大样本的非参数依赖性求和测度

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-12-17 DOI: 10.1016/j.csda.2024.108109

Kai Xu , Qing Cheng , Daojiang He

{"title":"On summed nonparametric dependence measures in high dimensions, fixed or large samples","authors":"Kai Xu , Qing Cheng , Daojiang He","doi":"10.1016/j.csda.2024.108109","DOIUrl":"10.1016/j.csda.2024.108109","url":null,"abstract":"<div><div>For the mutual independence testing problem, the use of summed nonparametric dependence measures, including Hoeffding's <em>D</em>, Blum-Kiefer-Rosenblatt's <em>R</em>, Bergsma-Dassios-Yanagimoto's <span><math><msup><mrow><mi>τ</mi></mrow><mrow><mo>⁎</mo></mrow></msup></math></span>, is considered. The asymptotic normality of this class of test statistics for the null hypothesis is established when (i) both the dimension and the sample size go to infinity simultaneously, and (ii) the dimension tends to infinity but the sample size is fixed. The new result for the asymptotic regime (ii) is applicable to the HDLSS (High Dimension, Low Sample Size) data. Further, the asymptotic Pitman efficiencies of the family of considered tests are investigated with respect to two important sum-of-squares tests for the asymptotic regime (i): the distance covariance based test and the product-moment covariance based test. Formulae for asymptotic relative efficiencies are found. An interesting finding reveals that even if the population follows a normally distributed structure, the two state-of-art tests suffer from power loss if some components of the underlying data have different scales. Simulations are conducted to confirm our asymptotic results. A real data analysis is performed to illustrate the considered methods.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"205 ","pages":"Article 108109"},"PeriodicalIF":1.5,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161773","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}

引用次数: 0

Adaptive distributed smooth composite quantile regression estimation for large-scale data 大规模数据的自适应分布光滑复合分位数回归估计

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-12-13 DOI: 10.1016/j.csda.2024.108110

Kangning Wang, Jingyu Zhang, Xiaofei Sun

引用次数: 0

A subspace method for large-scale trace ratio problems 大规模迹比问题的子空间方法

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-12-13 DOI: 10.1016/j.csda.2024.108108

Giulia Ferrandi , Michiel E. Hochstenbach , M. Rosário Oliveira

引用次数: 0

Subgroup learning for multiple mixed-type outcomes with block-structured covariates 带有块结构协变量的多混合型结果的子组学习

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-12-02 DOI: 10.1016/j.csda.2024.108105

Xun Zhao , Lu Tang , Weijia Zhang , Ling Zhou

{"title":"Subgroup learning for multiple mixed-type outcomes with block-structured covariates","authors":"Xun Zhao , Lu Tang , Weijia Zhang , Ling Zhou","doi":"10.1016/j.csda.2024.108105","DOIUrl":"10.1016/j.csda.2024.108105","url":null,"abstract":"<div><div>The increasing interest in survey research focuses on inferring grouped association patterns between risk factors and questionnaire responses, with grouping shared across multiple response variables that jointly capture one's underlying status. Aiming to identify important risk factors that are simultaneously associated with the health and well-being of senior adults, a study based on the China Health and Retirement Survey (CHRS) is conducted. Previous studies have identified several known risk factors, yet heterogeneity in the outcome-risk factor association exists, prompting the use of subgroup analysis. A subgroup analysis procedure is devised to model a multiple mixed-type outcome which describes one's general health and well-being, while tackling additional challenges including collinearity and weak signals within block-structured covariates. Computationally, an efficient algorithm that alternately updates a set of estimating equations and likelihood functions is proposed. Theoretical results establish the asymptotic consistency and normality of the proposed estimators. The validity of the proposed method is corroborated by simulation experiments. An application of the proposed method to the CHRS data identifies caring for grandchildren as a new risk factor for poor physical and mental health.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"204 ","pages":"Article 108105"},"PeriodicalIF":1.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143150516","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}

引用次数: 0

A copula duration model with dependent states and spells 具有依赖状态和符咒的联结持续时间模型

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-11-28 DOI: 10.1016/j.csda.2024.108104

Simon M.S. Lo , Shuolin Shi , Ralf A. Wilke

引用次数: 0

Development of a new general class of bivariate distributions based on reversed hazard rate order 基于逆向风险率顺序的一类新的二元分布的发展

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-11-26 DOI: 10.1016/j.csda.2024.108106

Na Young Yoo , Hyunju Lee , Ji Hwan Cha

引用次数: 0

Multi-task optimization with Bayesian neural network surrogates for parameter estimation of a simulation model 利用贝叶斯神经网络代理进行多任务优化，以估算仿真模型参数

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-11-22 DOI: 10.1016/j.csda.2024.108097

Hyungjin Kim , Chuljin Park , Heeyoung Kim

{"title":"Multi-task optimization with Bayesian neural network surrogates for parameter estimation of a simulation model","authors":"Hyungjin Kim , Chuljin Park , Heeyoung Kim","doi":"10.1016/j.csda.2024.108097","DOIUrl":"10.1016/j.csda.2024.108097","url":null,"abstract":"<div><div>We propose a novel framework for efficient parameter estimation in simulation models, formulated as an optimization problem that minimizes the discrepancy between physical system observations and simulation model outputs. Our framework, called multi-task optimization with Bayesian neural network surrogates (MOBS), is designed for scenarios that require the simultaneous estimation of multiple sets of parameters, each set corresponding to a distinct set of observations, while also enabling fast parameter estimation essential for real-time process monitoring and control. MOBS integrates a heuristic search algorithm, utilizing a single-layer Bayesian neural network surrogate model trained on an initial simulation dataset. This surrogate model is shared across multiple tasks to select and evaluate candidate parameter values, facilitating efficient multi-task optimization. We provide a closed-form parameter screening rule and demonstrate that the expected number of simulation runs converges to a user-specified threshold. Our framework was applied to a numerical example and a semiconductor manufacturing case study, significantly reducing computational costs while achieving accurate parameter estimation.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"204 ","pages":"Article 108097"},"PeriodicalIF":1.5,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701344","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}

引用次数: 0

Optimal sequential detection by sparsity likelihood 利用稀疏似然法优化顺序检测

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-11-17 DOI: 10.1016/j.csda.2024.108089

Jingyan Huang, Hock Peng Chan

引用次数: 0