Computational Statistics & Data Analysis最新文献

Weighted support vector machine for extremely imbalanced data 用于极端不平衡数据的加权支持向量机

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-11-04 DOI: 10.1016/j.csda.2024.108078

Jongmin Mun , Sungwan Bang , Jaeoh Kim

引用次数: 0

Cox regression model with doubly truncated and interval-censored data 双截断数据和区间截断数据的 Cox 回归模型

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-11-04 DOI: 10.1016/j.csda.2024.108090

Pao-sheng Shen

引用次数: 0

Accelerating computation: A pairwise fitting technique for multivariate probit models 加速计算：多元概率模型的成对拟合技术

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-10-31 DOI: 10.1016/j.csda.2024.108082

Margaux Delporte , Geert Verbeke , Steffen Fieuws , Geert Molenberghs

引用次数: 0

A unified consensus-based parallel algorithm for high-dimensional regression with combined regularizations 基于共识的高维回归并行统一算法与组合正则化

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-10-30 DOI: 10.1016/j.csda.2024.108081

Xiaofei Wu , Rongmei Liang , Zhimin Zhang , Zhenyu Cui

{"title":"A unified consensus-based parallel algorithm for high-dimensional regression with combined regularizations","authors":"Xiaofei Wu , Rongmei Liang , Zhimin Zhang , Zhenyu Cui","doi":"10.1016/j.csda.2024.108081","DOIUrl":"10.1016/j.csda.2024.108081","url":null,"abstract":"<div><div>The parallel algorithm is widely recognized for its effectiveness in handling large-scale datasets stored in a distributed manner, making it a popular choice for solving statistical learning models. However, there is currently limited research on parallel algorithms specifically designed for high-dimensional regression with combined regularization terms. These terms, such as elastic-net, sparse group lasso, sparse fused lasso, and their nonconvex variants, have gained significant attention in various fields due to their ability to incorporate prior information and promote sparsity within specific groups or fused variables. The scarcity of parallel algorithms for combined regularizations can be attributed to the inherent nonsmoothness and complexity of these terms, as well as the absence of closed-form solutions for certain proximal operators associated with them. This paper proposes a <em>unified</em> constrained optimization formulation based on the consensus problem for these types of convex and nonconvex regression problems, and derives the corresponding parallel alternating direction method of multipliers (ADMM) algorithms. Furthermore, it is proven that the proposed algorithm not only has global convergence but also exhibits a linear convergence rate. It is worth noting that the computational complexity of the proposed algorithm remains the same for different regularization terms and losses, which implicitly demonstrates the universality of this algorithm. Extensive simulation experiments, along with a financial example, serve to demonstrate the reliability, stability, and scalability of our algorithm. The R package for implementing the proposed algorithm can be obtained at <span><span>https://github.com/xfwu1016/CPADMM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587410","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

Multi-model subset selection 多模型子集选择

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-10-29 DOI: 10.1016/j.csda.2024.108073

Anthony-Alexander Christidis , Stefan Van Aelst , Ruben Zamar

{"title":"Multi-model subset selection","authors":"Anthony-Alexander Christidis , Stefan Van Aelst , Ruben Zamar","doi":"10.1016/j.csda.2024.108073","DOIUrl":"10.1016/j.csda.2024.108073","url":null,"abstract":"<div><div>The two primary approaches for high-dimensional regression problems are sparse methods (e.g., best subset selection, which uses the <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>-norm in the penalty) and ensemble methods (e.g., random forests). Although sparse methods typically yield interpretable models, in terms of prediction accuracy they are often outperformed by “blackbox” multi-model ensemble methods. A regression ensemble is introduced which combines the interpretability of sparse methods with the high prediction accuracy of ensemble methods. An algorithm is proposed to solve the joint optimization of the corresponding <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>-penalized regression models by extending recent developments in <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>-optimization for sparse methods to multi-model regression ensembles. The sparse and diverse models in the ensemble are learned simultaneously from the data. Each of these models provides an explanation for the relationship between a subset of predictors and the response variable. Empirical studies and theoretical knowledge about ensembles are used to gain insight into the ensemble method's performance, focusing on the interplay between bias, variance, covariance, and variable selection. In prediction tasks, the ensembles can outperform state-of-the-art competitors on both simulated and real data. Forward stepwise regression is also generalized to multi-model regression ensembles and used to obtain an initial solution for the algorithm. The optimization algorithms are implemented in publicly available software packages.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142560769","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

Vine copula based structural equation models 基于藤蔓协程的结构方程模型

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-10-26 DOI: 10.1016/j.csda.2024.108076

Claudia Czado

引用次数: 0

Bayesian grouping-Gibbs sampling estimation of high-dimensional linear model with non-sparsity 非稀疏性高维线性模型的贝叶斯分组-吉布斯抽样估计

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-10-23 DOI: 10.1016/j.csda.2024.108072

Shanshan Qin , Guanlin Zhang , Yuehua Wu , Zhongyi Zhu

{"title":"Bayesian grouping-Gibbs sampling estimation of high-dimensional linear model with non-sparsity","authors":"Shanshan Qin , Guanlin Zhang , Yuehua Wu , Zhongyi Zhu","doi":"10.1016/j.csda.2024.108072","DOIUrl":"10.1016/j.csda.2024.108072","url":null,"abstract":"<div><div>In high-dimensional linear regression models, common assumptions typically entail sparsity of regression coefficients <span><math><mi>β</mi><mo>∈</mo><msup><mrow><mi>R</mi></mrow><mrow><mi>p</mi></mrow></msup></math></span>. However, these assumptions may not hold when the majority, if not all, of regression coefficients are non-zeros. Statistical methods designed for sparse models may lead to substantial bias in model estimation. Therefore, this article proposes a novel Bayesian Grouping-Gibbs Sampling (BGGS) method, which departs from the common sparse assumptions in high-dimensional problems. The BGGS method leverages a grouping strategy that partitions <strong><em>β</em></strong> into distinct groups, facilitating rapid sampling in high-dimensional space. The grouping number (<em>k</em>) can be determined using the ‘Elbow plot’, which operates efficiently and is robust against the initial value. Theoretical analysis, under some regular conditions, guarantees model selection and parameter estimation consistency, and bound for the prediction error. Furthermore, three finite simulations are conducted to assess the competitive advantages of the proposed method in terms of parameter estimation and prediction accuracy. Finally, the BGGS method is applied to a financial dataset to explore its practical utility.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529305","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 comparative analysis of different adjustment sets using propensity score based estimators 使用基于倾向分数的估算器对不同调整集进行比较分析

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-10-21 DOI: 10.1016/j.csda.2024.108079

Shanshan Luo , Jiaqi Min , Wei Li , Xueli Wang , Zhi Geng

引用次数: 0

Unified specification tests in partially linear time series models 部分线性时间序列模型的统一规格检验

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-10-17 DOI: 10.1016/j.csda.2024.108074

Shuang Sun , Zening Song , Xiaojun Song

{"title":"Unified specification tests in partially linear time series models","authors":"Shuang Sun , Zening Song , Xiaojun Song","doi":"10.1016/j.csda.2024.108074","DOIUrl":"10.1016/j.csda.2024.108074","url":null,"abstract":"<div><div>Based on a residual marked empirical process, Cramér–von Mises and Kolmogorov–Smirnov tests are proposed for the correct specification of the nonparametric components in partially linear time series models. The tests are unified in the sense that the asymptotic distribution of residual marked empirical process is invariant across different <span><math><msup><mrow><mi>n</mi></mrow><mrow><mi>ν</mi></mrow></msup></math></span>-consistent estimators in calculating residuals (where <span><math><mi>ν</mi><mo>></mo><mn>1</mn><mo>/</mo><mn>4</mn></math></span>) under the null. In addition, the residual marked empirical process has the same power property under the sequence of local alternatives regardless of the estimators used. Achieved through a projection method, these features also enable using a computationally convenient multiplier bootstrap to approximate the unified null distributions of the test statistics. Simulations show satisfactory finite-sample performance of the proposed method. The application to validate the parametric form of conditional variance in the ARCH-X model is also highlighted, along with an empirical analysis of the conditional variance of the FTSE 100 index return series.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529303","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

Testing sufficiency for transfer learning 测试迁移学习的充分性

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-10-16 DOI: 10.1016/j.csda.2024.108075

Ziqian Lin , Yuan Gao , Feifei Wang , Hansheng Wang

{"title":"Testing sufficiency for transfer learning","authors":"Ziqian Lin , Yuan Gao , Feifei Wang , Hansheng Wang","doi":"10.1016/j.csda.2024.108075","DOIUrl":"10.1016/j.csda.2024.108075","url":null,"abstract":"<div><div>Modern statistical analysis often encounters high dimensional models but with limited sample sizes. This makes it difficult to estimate high-dimensional statistical models based on target data with limited sample size. Then how to borrow information from another large sized source data for more accurate target model estimation becomes an interesting problem. This leads to the useful idea of transfer learning. Various estimation methods in this regard have been developed recently. In this work, we study transfer learning from a different perspective. Specifically, we consider here the problem of testing for transfer learning sufficiency. We denote <em>transfer learning sufficiency</em> to be the null hypothesis. It refers to the situation that, with the help of the source data, the useful information contained in the feature vectors of the target data can be sufficiently extracted for predicting the interested target response. Therefore, the rejection of the null hypothesis implies that information useful for prediction remains in the feature vectors of the target data and thus calls for further exploration. To this end, we develop a novel testing procedure and a centralized and standardized test statistic, whose asymptotic null distribution is analytically derived. Simulation studies are presented to demonstrate the finite sample performance of the proposed method. A deep learning related real data example is presented for illustration purpose.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529302","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