Journal of Statistical Planning and Inference最新文献

Accelerated failure time model under dependent truncated data 相关截断数据下的加速失效时间模型

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-05-27 DOI: 10.1016/j.jspi.2025.106297

Jin-Jian Hsieh, Siang-Ying Chen

引用次数: 0

Semiparametric modal regression with varying coefficients and measurement error 变系数半参数模态回归及测量误差

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-05-25 DOI: 10.1016/j.jspi.2025.106307

Aman Ullah , Tao Wang

{"title":"Semiparametric modal regression with varying coefficients and measurement error","authors":"Aman Ullah , Tao Wang","doi":"10.1016/j.jspi.2025.106307","DOIUrl":"10.1016/j.jspi.2025.106307","url":null,"abstract":"<div><div>We in this paper propose a stepwise estimation procedure for semiparametric modal regression with varying coefficients and measurement error, where the linear covariate is unobserved but an ancillary variable is available. This modal regression framework, which is built on the mode value rather than the mean, captures the “most likely” effect instead of the traditional average effect. The proposed stepwise procedure introduces a restricted regression mode by imposing a structural constraint on the model, allowing us to concentrate out the varying coefficients using the “correction for attenuation” method commonly employed in mean regression. This transformation reduces the original model to a parametric modal regression. We establish the consistency and asymptotic normality of the resulting modal estimators by analyzing the tail behavior of the characteristic function of the error distribution, distinguishing between ordinary smooth and super smooth cases. Additionally, we investigate bandwidth selection strategies and propose a simulation-extrapolation algorithm as a practical approach for optimal bandwidth choice. We conduct Monte Carlo simulations to assess the finite sample performance of the resulting estimators and present a real data analysis to further illustrate the effectiveness of the suggested estimation procedure.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"240 ","pages":"Article 106307"},"PeriodicalIF":0.8,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134836","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

Divide and conquer for generalized approximately expectile regression 广义近似期望回归的分治法

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-05-19 DOI: 10.1016/j.jspi.2025.106300

Zhen Zeng , Weixin Yao

引用次数: 0

Asymptotically efficient estimation under local constraint in Wicksell’s problem 局部约束下Wicksell问题的渐近有效估计

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-05-16 DOI: 10.1016/j.jspi.2025.106299

Francesco Gili, Geurt Jongbloed, Aad van der Vaart

{"title":"Asymptotically efficient estimation under local constraint in Wicksell’s problem","authors":"Francesco Gili, Geurt Jongbloed, Aad van der Vaart","doi":"10.1016/j.jspi.2025.106299","DOIUrl":"10.1016/j.jspi.2025.106299","url":null,"abstract":"<div><div>We consider nonparametric estimation of the distribution function <span><math><mi>F</mi></math></span> of squared sphere radii in the classical Wicksell problem. Under smoothness conditions on <span><math><mi>F</mi></math></span> in a neighborhood of <span><math><mi>x</mi></math></span>, in Gili et al. (2024) it is shown that the Isotonic Inverse Estimator (IIE) is asymptotically efficient and attains rate of convergence <span><math><msqrt><mrow><mi>n</mi><mo>/</mo><mo>log</mo><mi>n</mi></mrow></msqrt></math></span>. If <span><math><mi>F</mi></math></span> is constant on an interval containing <span><math><mi>x</mi></math></span>, the optimal rate of convergence increases to <span><math><msqrt><mrow><mi>n</mi></mrow></msqrt></math></span> and the IIE attains this rate adaptively, i.e. without explicitly using the knowledge of local constancy. However, in this case, the asymptotic distribution is not normal. In this paper, we introduce three <em>informed</em> projection-type estimators of <span><math><mi>F</mi></math></span>, which use knowledge on the interval of constancy and show these are all asymptotically equivalent and normal. Furthermore, we establish a local asymptotic minimax lower bound in this setting, proving that the three <em>informed</em> estimators are asymptotically efficient and a convolution result showing that the IIE is not efficient. We also derive the asymptotic distribution of the difference of the IIE with the efficient estimators, demonstrating that the IIE is <em>not</em> asymptotically equivalent to the <em>informed</em> estimators. Through a simulation study, we provide evidence that the performance of the IIE closely resembles that of its competitors, supporting the use of the IIE as the standard choice when no information about <span><math><mi>F</mi></math></span> is available.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"240 ","pages":"Article 106299"},"PeriodicalIF":0.8,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144088647","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

A nonparametric test for the heterogeneity of the spatial autoregressive parameter 空间自回归参数异质性的非参数检验

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-05-10 DOI: 10.1016/j.jspi.2025.106298

Yangbing Tang , Jiang Du , Zhongzhan Zhang

引用次数: 0

Copula-based semiparametric nonnormal transformed linear model for survival data with dependent censoring 基于copula的生存数据非正态变换线性模型

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-05-07 DOI: 10.1016/j.jspi.2025.106296

Huazhen Yu , Lixin Zhang

{"title":"Copula-based semiparametric nonnormal transformed linear model for survival data with dependent censoring","authors":"Huazhen Yu , Lixin Zhang","doi":"10.1016/j.jspi.2025.106296","DOIUrl":"10.1016/j.jspi.2025.106296","url":null,"abstract":"<div><div>Although the independent censoring assumption is commonly used in survival analysis, it can be violated when the censoring time is related to the survival time, which often happens in many practical applications. To address this issue, we propose a flexible semiparametric method for dependent censored data. Our approach involves fitting the survival time and the censoring time with a joint transformed linear model, where the transformed function is unspecified. This allows for a very general class of models that can account for possible covariate effects, while also accommodating administrative censoring. We assume that the transformed variables have a bivariate nonnormal distribution based on parametric copulas and parametric marginals, which further enhances the flexibility of our method. We demonstrate the identifiability of the proposed model and establish the consistency and asymptotic normality of the model parameters under appropriate regularity conditions and assumptions. Furthermore, we evaluate the performance of our method through extensive simulation studies, and provide a real data example for illustration.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"240 ","pages":"Article 106296"},"PeriodicalIF":0.8,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927463","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

Maximum Projection Gini Correlation (MaGiC) for mixed categorical and numerical data 混合分类和数值数据的最大投影基尼相关（MaGiC）

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-04-24 DOI: 10.1016/j.jspi.2025.106294

Hong Xiao , Radhakrishna Adhikari , Yixin Chen , Xin Dang

{"title":"Maximum Projection Gini Correlation (MaGiC) for mixed categorical and numerical data","authors":"Hong Xiao , Radhakrishna Adhikari , Yixin Chen , Xin Dang","doi":"10.1016/j.jspi.2025.106294","DOIUrl":"10.1016/j.jspi.2025.106294","url":null,"abstract":"<div><div>We propose a projection correlation for measure of dependence between numerical multivariate variables and categorical variables. The projection correlation, defined as the maximum of the Gini correlations (i.e., MaGiC) between the categorical variable and the univariate projections of the multivariate vector, is non-parametric, and intuitively produces a high coefficient when the two variables are dependent, and zero when they are independent. We show that MaGiC possesses the property of nestedness, in that it is non-decreasing with the increasing number of features in the numerical vector, while remaining unchanged if additional numerical features are independent of the categorical variable and original features. We establish <span><math><msqrt><mrow><mi>n</mi></mrow></msqrt></math></span>-consistency of the sample projection correlation. A powerful <span><math><mi>K</mi></math></span>-sample test can be carried out via the MaGiC-based independence test. When compared with related correlation definitions for multivariate variables, MaGiC also enjoys a faster implementation, with the computational complexity <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mi>m</mi><mi>n</mi><mrow><mo>(</mo><mi>d</mi><mo>+</mo><mo>log</mo><mi>n</mi><mo>)</mo></mrow><mo>)</mo></mrow></mrow></math></span> where <span><math><mi>d</mi></math></span> is the dimension of the numerical variable, <span><math><mi>n</mi></math></span> is the sample size, and <span><math><mi>m</mi></math></span> is the number of projections performed, as opposed to <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mi>d</mi><mspace></mspace><msup><mrow><mi>n</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> for Gini correlation. We demonstrate these properties through simulation and application to real datasets.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"239 ","pages":"Article 106294"},"PeriodicalIF":0.8,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874462","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

M-procedures robust to structural changes detection under strong mixing heavy-tailed time series models 在强混合重尾时间序列模型下，m程序对结构变化检测具有鲁棒性

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-04-24 DOI: 10.1016/j.jspi.2025.106295

Hao Jin , Jiating Hu , Ling Zhu , Shiyu Tian , Si Zhang

{"title":"M-procedures robust to structural changes detection under strong mixing heavy-tailed time series models","authors":"Hao Jin , Jiating Hu , Ling Zhu , Shiyu Tian , Si Zhang","doi":"10.1016/j.jspi.2025.106295","DOIUrl":"10.1016/j.jspi.2025.106295","url":null,"abstract":"<div><div>Many tests of change points resort to least squares estimation method, but it can lead to bias if these observations are heavy-tailed processes. The aim of this paper is to construct a ratio-typed test based on M-estimation, which avoids the long-range variance estimation and is robust to structural change detection under strong mixing series with heavy-tailed. The proposed test consisting of M-procedures has more utility in that it allows processes in the domain of attraction of a stable law with index <span><math><mrow><mi>κ</mi><mo>∈</mo><mrow><mo>(</mo><mn>0</mn><mo>,</mo><mn>2</mn><mo>)</mo></mrow></mrow></math></span>, not limited to <span><math><mrow><mo>(</mo><mn>1</mn><mo>,</mo><mn>2</mn><mo>)</mo></mrow></math></span>. Under some regular conditions, asymptotic distribution under the null hypothesis of no change is functional of a Brownian motion, and the divergent rate under the alternative hypothesis is also provided. Furthermore, the convergence rate of a ratio-typed change point estimator is established. Simulation study illustrates there is no distortion in empirical sizes, and empirical powers have satisfactory performance. Finally, two practical applications to real examples are presented as well.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"239 ","pages":"Article 106295"},"PeriodicalIF":0.8,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891417","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

Pursuing sparsity and homogeneity for multi-source high-dimensional current status data 追求多源高维现状数据的稀疏性和同质性

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-04-23 DOI: 10.1016/j.jspi.2025.106293

Xin Ye , Yanyan Liu

引用次数: 0

Neighborhood VAR: Efficient estimation of multivariate timeseries with neighborhood information 邻域VAR：具有邻域信息的多元时间序列的有效估计

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-03-31 DOI: 10.1016/j.jspi.2025.106277

Zhihao Hu , Shyam Ranganathan , Yang Shao , Xinwei Deng

引用次数: 0