Journal of Statistical Planning and Inference最新文献_第3页

Minimax designs for partially linear models 部分线性模型的极大极小设计

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-07-09 DOI: 10.1016/j.jspi.2025.106312

Shaohua Xu, Yongdao Zhou

{"title":"Minimax designs for partially linear models","authors":"Shaohua Xu, Yongdao Zhou","doi":"10.1016/j.jspi.2025.106312","DOIUrl":"10.1016/j.jspi.2025.106312","url":null,"abstract":"<div><div>Partially linear models are widely used in many scientific and engineering fields due to their flexibility and interpretability. However, the design of experiments for these models remains underexplored. This paper tackles the challenge of robust experimental design for partially linear models within a minimax framework, focusing on the simultaneous robustness of both the regression function and the basis function. We derive explicit forms of minimax designs for various scenarios, including partially linear models with and without interactions. These designs are shown to have analytical expressions, specifically as the product measure of the orthogonal array and the uniform measure. For practical implementation, we present the exact <span><math><mi>n</mi></math></span>-point minimax design based on the qualitative–quantitative discrepancy. Simulation results indicate that the proposed minimax designs are robust and efficient, even when the assumed model faces moderate or large contamination, or when the model is misspecified. Finally, the practical applicability of our minimax designs is demonstrated through a synthetic data based on the Quinidine Kinetics dataset.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"241 ","pages":"Article 106312"},"PeriodicalIF":0.8,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595498","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

Individual aliased effect number pattern for two-level designs and its applications 两级设计的个别混叠效应数模式及其应用

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-07-07 DOI: 10.1016/j.jspi.2025.106316

Shengli Zhao, Tao Sun

引用次数: 0

Bootstrap-based tests for the total time on test and the excess wealth orders 基于引导的测试的总测试时间和剩余财富顺序

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-06-30 DOI: 10.1016/j.jspi.2025.106315

Tommaso Lando, Sirio Legramanti

引用次数: 0

On semi-supervised estimation using exponential tilt mixture models 指数倾斜混合模型的半监督估计

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-06-29 DOI: 10.1016/j.jspi.2025.106314

Ye Tian , Xinwei Zhang , Zhiqiang Tan

引用次数: 0

Limit results for estimation of connectivity matrix in multi-layer stochastic block models 多层随机块模型连通性矩阵估计的极限结果

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-06-23 DOI: 10.1016/j.jspi.2025.106313

Wenqing Su , Xiao Guo , Ying Yang

{"title":"Limit results for estimation of connectivity matrix in multi-layer stochastic block models","authors":"Wenqing Su , Xiao Guo , Ying Yang","doi":"10.1016/j.jspi.2025.106313","DOIUrl":"10.1016/j.jspi.2025.106313","url":null,"abstract":"<div><div>Multi-layer networks arise naturally in various domains including biology, finance and sociology, among others. The multi-layer stochastic block model (multi-layer SBM) is commonly used for community detection in the multi-layer networks. Most of current literature focuses on statistical consistency of community detection methods under multi-layer SBMs. However, the asymptotic distributional properties are also indispensable which play an important role in statistical inference. In this work, we aim to study the estimation and asymptotic properties of the layer-wise scaled connectivity matrices in the multi-layer SBM. We study and analyze a computationally tractable method for estimating the scaled connectivity matrices. Under the multi-layer SBM and its variant multi-layer degree-corrected SBM, we establish the asymptotic normality of the estimated matrices under mild conditions, which can be used for interval estimation and hypothesis testing. Simulations show the superior performance of proposed method over existing methods in two considered statistical inference tasks. We apply the method to a real dataset and obtain interpretable results. In addition, we develop a moment estimator for the non-scaled connectivity matrices and study its asymptotic properties.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"241 ","pages":"Article 106313"},"PeriodicalIF":0.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144500917","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

Deconvolution density estimation using penalized splines 利用惩罚样条进行反褶积密度估计

IF 0.8 4区数学

Journal of Statistical Planning and Inference Pub Date : 2025-06-17 DOI: 10.1016/j.jspi.2025.106310

Hanxiao Jing , Mary C. Meyer , Jiayang Sun

引用次数: 0

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