Journal of Statistical Planning and Inference最新文献

Variable selection in high-dimensional varying coefficient panel data models with fixed effects 固定效应高维变系数面板数据模型的变量选择

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-09-30 DOI: 10.1016/j.jspi.2025.106355

Yiping Yang , Peixin Zhao

引用次数: 0

Causal inference in early phase clinical trials: Variance decomposition and order of patient inclusion 早期临床试验的因果推断：方差分解和患者纳入顺序

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-09-29 DOI: 10.1016/j.jspi.2025.106352

Matthieu Clertant , Meliha Akouba , Alexia Iasonos , John O’Quigley

引用次数: 0

The k-sample Behrens-Fisher problem for high-dimensional data with model free assumption 具有无模型假设的高维数据k样本Behrens-Fisher问题

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-09-27 DOI: 10.1016/j.jspi.2025.106354

Yanbo Pei, Xiaoxiao Ren, Baoxue Zhang

{"title":"The k-sample Behrens-Fisher problem for high-dimensional data with model free assumption","authors":"Yanbo Pei, Xiaoxiao Ren, Baoxue Zhang","doi":"10.1016/j.jspi.2025.106354","DOIUrl":"10.1016/j.jspi.2025.106354","url":null,"abstract":"<div><div>The problem of testing the equality of <em>k</em>-sample mean vectors with different covariance matrices, known as the Behrens-Fisher (BF) problem for <em>k</em>-sample, is a significant issue in statistics. Hu and Bai (2017) proposed a test statistic that operates under a factor-like model structure assumption and demonstrated its normal limit. Building on this work, we further explore the asymptotic properties of the test statistic. We prove that the asymptotic null distribution of the test statistic is a Chi-square-type mixture distribution under a model-free assumption and establish its asymptotic power under a full alternative hypothesis. Moreover, we show that the asymptotic null distribution of the test statistic is either normal or a weighted sum of normal and Chi-square random variables, depending on the convergence rate of the eigenvalues of the covariance matrix with model free assumption. To address practical challenges in high-dimensional data, we propose a new weighted bootstrap procedure that is simple to implement. Simulation studies demonstrate that our proposed test procedure outperforms existing methods in terms of size control under various settings. Furthermore, real data applications illustrate the applicability of our test procedure to a variety of high-dimensional data analysis problems.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"242 ","pages":"Article 106354"},"PeriodicalIF":0.8,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220648","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

Joint distribution of numbers of occurrences of countably many runs of specified lengths in a sequence of discrete random variables 离散随机变量序列中指定长度的可数多次运行的出现次数的联合分布

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-09-25 DOI: 10.1016/j.jspi.2025.106353

Kiyoshi Inoue

引用次数: 0

Orthogonal Latin hypercube designs with hidden low-dimensional projection 具有隐藏低维投影的正交拉丁超立方体设计

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-09-24 DOI: 10.1016/j.jspi.2025.106349

Tian-fang Zhang , Yue-ru Yan , Fasheng Sun

引用次数: 0

Robust and computationally efficient gradient-based estimation 稳健且计算效率高的梯度估计

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-09-22 DOI: 10.1016/j.jspi.2025.106351

Yibo Yan , Xiaozhou Wang , Riquan Zhang

{"title":"Robust and computationally efficient gradient-based estimation","authors":"Yibo Yan , Xiaozhou Wang , Riquan Zhang","doi":"10.1016/j.jspi.2025.106351","DOIUrl":"10.1016/j.jspi.2025.106351","url":null,"abstract":"<div><div>In this paper, we propose a class of estimators based on the robust and computationally efficient gradient estimation for both low- and high-dimensional risk minimization framework. The gradient estimation in this work is constructed using a series of newly proposed univariate robust and efficient mean estimators. Our proposed estimators are obtained iteratively using a variant of the gradient descent method, where the update direction is determined by a robust and computationally efficient gradient. These estimators not only have explicit expressions and can be obtained through arithmetic operations but are also robust to arbitrary outliers in common statistical models. Theoretically, we establish the convergence of the algorithms and derive non-asymptotic error bounds for these iterative estimators. Specifically, we apply our methods to linear and logistic regression models, achieving robust parameter estimates and corresponding excess risk bounds. Unlike previous work, our theoretical results rely on a magnitude function of the outliers, which captures the extent of their deviation from the inliers. Finally, we present extensive simulation experiments on both low- and high-dimensional linear models to demonstrate the superior performance of our proposed estimators compared to several baseline methods.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"242 ","pages":"Article 106351"},"PeriodicalIF":0.8,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158353","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

Assessing goodness-of-fit for sparse categories using Rényi divergence 利用rsamnyi散度评估稀疏分类的拟合优度

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-09-19 DOI: 10.1016/j.jspi.2025.106350

Raul Matsushita , Gabriel Gomes , Regina Da Fonseca , Eduardo Nakano , Roberto Vila

{"title":"Assessing goodness-of-fit for sparse categories using Rényi divergence","authors":"Raul Matsushita , Gabriel Gomes , Regina Da Fonseca , Eduardo Nakano , Roberto Vila","doi":"10.1016/j.jspi.2025.106350","DOIUrl":"10.1016/j.jspi.2025.106350","url":null,"abstract":"<div><div>We present the Rényi divergence as a statistic for assessing goodness-of-fit in sparse frequency tables, where small expected counts can undermine the reliability of the traditional chi-square test. The Rényi divergence with index in (0,1) is a natural choice because it circumvents division-related issues by small frequencies. Our main result demonstrates that the Rényi statistic asymptotically follows a chi-square distribution. Through theoretical insights and Monte Carlo simulations, we evaluate the performance of the Rényi statistic across various values of the divergence index. We find that smaller index values improve the alignment of the Rényi statistic with the chi-square distribution and enhance its performance in sparse data settings. Additionally, the Rényi statistic exhibits good power properties in detecting deviations from the null hypothesis under these conditions. To illustrate its practical applicability, we present two real-world data analyses, highlighting the robustness of the Rényi divergence in scenarios involving sparse categories.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"242 ","pages":"Article 106350"},"PeriodicalIF":0.8,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105738","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

Consistent community detection approach in the nonparametric weighted stochastic blockmodel with unspecified number of communities 非参数加权随机块模型中未指定社团数的一致性社团检测方法

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-09-13 DOI: 10.1016/j.jspi.2025.106339

Fei Ye , Jingsong Xiao , Weidong Ma , Yulai Miao , Ying Yang

{"title":"Consistent community detection approach in the nonparametric weighted stochastic blockmodel with unspecified number of communities","authors":"Fei Ye , Jingsong Xiao , Weidong Ma , Yulai Miao , Ying Yang","doi":"10.1016/j.jspi.2025.106339","DOIUrl":"10.1016/j.jspi.2025.106339","url":null,"abstract":"<div><div>The stochastic blockmodel (SBM) is a widely used model for representing graphs. Numerous approaches have been applied to the SBM to detect latent community structures in graphs, typically using two types of consistency (strong and weak) to evaluate their performance. Most of these methods have been studied and shown to be consistent under the SBM framework. However, the consistency of the weighted SBM, an important extension of the SBM, has been largely overlooked. Moreover, few approaches are capable of detecting communities when the number of communities is unknown. In this paper, we propose a nonparametric method for effective community detection under the assortative, nonparametric weighted SBM with an unknown number of communities, and we establish the consistency of our approach. We introduce a novel concept, “consistency in relationship”, as a more practical criterion to assess the performance of community detection algorithms. Since solving the optimization problem in our approach becomes intractable for large sample sizes, we propose an efficient algorithm to approximate it. Simulations demonstrate that our community detection method is both efficient and robust, particularly for unbalanced networks. We illustrate the effectiveness of our approach on three real-world networks.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"242 ","pages":"Article 106339"},"PeriodicalIF":0.8,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105737","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

D-criterion based optimal subsampling in Poisson regression with one covariate 单协变量泊松回归中基于d准则的最优子抽样

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-09-12 DOI: 10.1016/j.jspi.2025.106340

Torsten Glemser, Rainer Schwabe

引用次数: 0

Structured regularization covariance estimation in tensor-valued data analysis 张量值数据分析中的结构化正则化协方差估计

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-09-06 DOI: 10.1016/j.jspi.2025.106337

Jiangyan Wang, Yang Ren, Jinguan Lin

{"title":"Structured regularization covariance estimation in tensor-valued data analysis","authors":"Jiangyan Wang, Yang Ren, Jinguan Lin","doi":"10.1016/j.jspi.2025.106337","DOIUrl":"10.1016/j.jspi.2025.106337","url":null,"abstract":"<div><div>Covariance estimation poses a crucial challenge in high-dimensional data analysis, especially when traditional methods (e.g., sample covariance) are inaccurate, particularly with small sample sizes. A promising solution is to exploit inherent data structures such as low-rankness, sparsity, or smoothness. For tensor data (multi-dimensional arrays), structured regularization aids in dimensionality reduction. This paper introduces novel regularization methods for tensor covariance estimation, specifically applying banded and tapering structures to the covariance matrix. We use Kronecker Product Canonical Polyadic (KPCP) decomposition to approximate large matrices via the Kronecker product of smaller matrices. A split resampling scheme is employed to select parameters for the KPCP decomposition from noisy data. This leads to two methods: KPCP-TB-R (Triply Banded-Resampling) and KPCP-TT-R (Triply Tapering-Resampling). Additionally, sparse (thresholding) and multi-structured regularization approaches are introduced for comparison. The effectiveness and robustness of the proposed methods are validated through extensive simulations and applied to monthly export trade volume data.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"242 ","pages":"Article 106337"},"PeriodicalIF":0.8,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049542","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