发布求助
文献互助 智能选刊 最新文献

Annals of the Institute of Statistical Mathematics最新文献

筛选
英文 中文
A way of eliminating a nuisance parameter with the plug-in method utilizing an independent sample 一种利用独立样本用插入式方法消除干扰参数的方法
IF 0.6 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2025-04-15 DOI: 10.1007/s10463-025-00927-y
George Tzavelas
{"title":"A way of eliminating a nuisance parameter with the plug-in method utilizing an independent sample","authors":"George Tzavelas","doi":"10.1007/s10463-025-00927-y","DOIUrl":"10.1007/s10463-025-00927-y","url":null,"abstract":"<div><p>The estimation of the structural parameter in the presence of a nuisance parameter is an old and challenging problem. The usual estimating method is the plug-in likelihood method, using the same data set for estimating both the structural as well as the nuisance parameters. The aim of this paper is to provide an optimal estimating function for the estimation of the parameter of interest using the plug-in method, when an estimator for the nuisance parameter is available independent of the sample used to estimate the structural parameter.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 4","pages":"627 - 648"},"PeriodicalIF":0.6,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145143756","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 distance covariance test of independence in high dimension, low sample size contexts 在高维、低样本量背景下的独立性距离协方差检验
IF 0.6 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2025-04-10 DOI: 10.1007/s10463-025-00928-x
Kai Xu, Minghui Yang
{"title":"A distance covariance test of independence in high dimension, low sample size contexts","authors":"Kai Xu,&nbsp;Minghui Yang","doi":"10.1007/s10463-025-00928-x","DOIUrl":"10.1007/s10463-025-00928-x","url":null,"abstract":"<div><p>To check the mutual independence of a high-dimensional random vector without Gaussian assumption, Yao et al. (Journal of the Royal Statistical Society Series B, 80,455–480, 2018) recently introduced an important test by virtue of pairwise distance covariances. Despite its usefulness, the state-of-art test tends to have unsatisfactory size performance when the sample size is small. The present paper provides a theoretical explanation about this phenomenon, and accordingly proposes a new test in high dimension, low sample size contexts. The new test can be even justified as the dimension tends to infinity, regardless of whether the sample size is fixed or diverges. The power of the proposed distance covariance test is also investigated. To examine our theoretical findings and check the performance of the new test, simulation studies are applied. We further illustrate the proposed method by empirical analysis of a real dataset.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 5","pages":"731 - 755"},"PeriodicalIF":0.6,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923192","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
Uniformly consistent proportion estimation for composite hypotheses via integral equations: “the case of Gamma random variables” 基于积分方程的复合假设的一致一致比例估计：“Gamma随机变量的情况”
IF 0.6 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2025-04-04 DOI: 10.1007/s10463-025-00930-3
Xiongzhi Chen
{"title":"Uniformly consistent proportion estimation for composite hypotheses via integral equations: “the case of Gamma random variables”","authors":"Xiongzhi Chen","doi":"10.1007/s10463-025-00930-3","DOIUrl":"10.1007/s10463-025-00930-3","url":null,"abstract":"<div><p>We consider estimating the proportion of random variables for two types of composite null hypotheses: (i) the means of the random variables belonging to a non-empty, bounded interval; (ii) the means of the random variables belonging to an unbounded interval that is not the whole real line. For each type of composite null hypotheses, uniformly consistent estimators of the proportion of false null hypotheses are constructed for random variables whose distributions are members of the Gamma family. Further, uniformly consistent estimators of certain functions of a bounded null on the means are provided for the random variables mentioned earlier. These functions are continuous and of bounded variation. The estimators are constructed via solutions to Lebesgue-Stieltjes integral equations and harmonic analysis, do not rely on a concept of <i>p</i>-value, and have various applications.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 4","pages":"649 - 684"},"PeriodicalIF":0.6,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145142598","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
Tuning parameter selection for the adaptive nuclear norm regularized trace regression 自适应核范数正则化轨迹回归的参数选择
IF 0.8 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2025-03-27 DOI: 10.1007/s10463-025-00926-z
Yiting Ma, Pan Shang, Lingchen Kong
{"title":"Tuning parameter selection for the adaptive nuclear norm regularized trace regression","authors":"Yiting Ma,&nbsp;Pan Shang,&nbsp;Lingchen Kong","doi":"10.1007/s10463-025-00926-z","DOIUrl":"10.1007/s10463-025-00926-z","url":null,"abstract":"<div><p>Regularized models have been applied in lots of areas in recent years, with high dimensional data sets being popular. Because that tuning parameter decides the theoretical performance and computational efficiency of the regularized models, tuning parameter selection is a basic and important issue. We consider the tuning parameter selection for adaptive nuclear norm regularized trace regression, which achieves by the Bayesian information criterion (BIC). The proposed BIC is established with the help of an unbiased estimator of degrees of freedom. Under some regularized conditions, this BIC is proved to achieve the rank consistency of the tuning parameter selection. That is the model solution under selected tuning parameter converges to the true solution and has the same rank with that of the true solution in probability. Some numerical results are presented to evaluate the performance of the proposed BIC on tuning parameter selection.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 3","pages":"491 - 516"},"PeriodicalIF":0.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879626","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
On uniform consistency of nonparametric estimators smoothed by the gamma kernel 核光滑非参数估计量的一致相合性
IF 0.8 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2025-02-17 DOI: 10.1007/s10463-024-00923-8
Benedikt Funke, Masayuki Hirukawa
{"title":"On uniform consistency of nonparametric estimators smoothed by the gamma kernel","authors":"Benedikt Funke,&nbsp;Masayuki Hirukawa","doi":"10.1007/s10463-024-00923-8","DOIUrl":"10.1007/s10463-024-00923-8","url":null,"abstract":"<div><p>This paper documents a set of uniform consistency results with rates for nonparametric density and regression estimators smoothed by the gamma kernel having support on the nonnegative real line. It is known that this kernel can well calibrate the shapes of ‘cost’ distributions that are characterized by a sharp peak in the vicinity of the origin and a long right tail. In this paper, weak and strong uniform consistency and corresponding convergence rates of gamma kernel estimators are explored in a multivariate framework. Our analysis is built on compact sets expanding to the nonnegative orthant and general sequences of smoothing parameters. The results are useful for asymptotic analysis of two-step semiparametric estimation using a first-step kernel estimate as a plug-in.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 3","pages":"459 - 489"},"PeriodicalIF":0.8,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879616","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
Score test for unconfoundedness under a logistic treatment assignment model logistic处理分配模型下的非混杂性得分检验
IF 0.6 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2025-02-08 DOI: 10.1007/s10463-024-00919-4
Hairu Wang, Yukun Liu, Haiying Zhou
{"title":"Score test for unconfoundedness under a logistic treatment assignment model","authors":"Hairu Wang,&nbsp;Yukun Liu,&nbsp;Haiying Zhou","doi":"10.1007/s10463-024-00919-4","DOIUrl":"10.1007/s10463-024-00919-4","url":null,"abstract":"<div><p>In the potential outcomes framework for causal inference, the most commonly adopted assumption to identify causal effects is unconfoundedness, namely the potential outcomes are conditionally independent of the treatment assignment given a set of covariates. A natural question is whether this assumption is valid given data. This problem is challenging as only one of the potential outcomes can be observed for each individual. Under a logistic treatment assignment model and parametric regression models on the potential outcomes, we develop a score test for this problem and establish its limiting distribution. A remarkable advantage of our test is that its implementation requires only parameter estimation under the null unconfoundedness assumption and hence bypasses the identification issue. Our numerical results show that the score test has well-controlled type I errors and desirable powers.\u0000</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 4","pages":"517 - 533"},"PeriodicalIF":0.6,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145142976","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
Semiparametric transformation models for survival data with dependent censoring 具有相关删减的生存数据半参数变换模型
IF 0.8 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2024-12-26 DOI: 10.1007/s10463-024-00921-w
Negera Wakgari Deresa, Ingrid Van Keilegom
{"title":"Semiparametric transformation models for survival data with dependent censoring","authors":"Negera Wakgari Deresa,&nbsp;Ingrid Van Keilegom","doi":"10.1007/s10463-024-00921-w","DOIUrl":"10.1007/s10463-024-00921-w","url":null,"abstract":"<div><p>This paper proposes copula based semiparametric transformation models to take dependent censoring into account. The model is based on a parametric Archimedean copula model for the relation between the survival time (<span>(T_1)</span>) and the censoring time (<span>(T_2)</span>), whereas the marginal distributions of <span>(T_1)</span> and <span>(T_2)</span> follow a semiparametric transformation model. We show that this flexible model is identified based on the distribution of the observable variables, and propose estimators of the nonparametric functions and the finite dimensional parameters. An estimation algorithm is provided for implementing the new method. We establish the asymptotic properties of the estimators of the model parameters and the nonparametric functions. The theoretical development can serve as a valuable template when dealing with estimating equations that involve systems of linear differential equations. We also investigate the performance of the proposed method using finite sample simulations and real data example.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 3","pages":"425 - 457"},"PeriodicalIF":0.8,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879581","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
Robust and efficient parameter estimation for discretely observed stochastic processes 离散观测随机过程的鲁棒有效参数估计
IF 0.8 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2024-12-23 DOI: 10.1007/s10463-024-00922-9
Rohan Hore, Abhik Ghosh
{"title":"Robust and efficient parameter estimation for discretely observed stochastic processes","authors":"Rohan Hore,&nbsp;Abhik Ghosh","doi":"10.1007/s10463-024-00922-9","DOIUrl":"10.1007/s10463-024-00922-9","url":null,"abstract":"<div><p>In various practical situations, we encounter data from stochastic processes which can be efficiently modeled by an appropriate parametric model for subsequent statistical analyses. Unfortunately, maximum likelihood (ML) estimation, the most common approach, is sensitive to slight model deviations or data contamination due to its well-known lack of robustness. Since the non-parametric alternatives often sacrifice efficiency, in this paper we develop a robust parameter estimation procedure for discretely observed data from a parametric stochastic process model which exploits the nice properties of the popular density power divergence measure. In particular, here we define the minimum density power divergence estimators (MDPDE) for the independent increment and the Markov processes. We establish the asymptotic consistency and distributional results for the proposed MDPDEs in these dependent stochastic process setups and illustrate their benefits over the usual ML estimator for common examples like the Poisson process, drifted Brownian motion and the auto-regressive models.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 3","pages":"387 - 424"},"PeriodicalIF":0.8,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879584","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
Confidence bounds for the true discovery proportion based on the exact distribution of the number of rejections 真实发现比例的置信限基于拒绝数量的精确分布
IF 0.8 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2024-12-13 DOI: 10.1007/s10463-024-00920-x
Friederike Preusse, Anna Vesely, Thorsten Dickhaus
{"title":"Confidence bounds for the true discovery proportion based on the exact distribution of the number of rejections","authors":"Friederike Preusse,&nbsp;Anna Vesely,&nbsp;Thorsten Dickhaus","doi":"10.1007/s10463-024-00920-x","DOIUrl":"10.1007/s10463-024-00920-x","url":null,"abstract":"<div><p>In multiple hypotheses testing it has become widely popular to make inference on the true discovery proportion (TDP) of a set <span>(mathscr {M})</span> of null hypotheses. This approach is useful for several application fields, such as neuroimaging and genomics. Several procedures to compute simultaneous lower confidence bounds for the TDP have been suggested in prior literature. Simultaneity allows for post-hoc selection of <span>(mathscr {M})</span>. If sets of interest are specified a priori, it is possible to gain power by removing the simultaneity requirement. We present an approach to compute lower confidence bounds for the TDP if the set of null hypotheses is defined a priori. The proposed method determines the bounds using the exact distribution of the number of rejections based on a step-up multiple testing procedure under independence assumptions. We assess robustness properties of our procedure and apply it to real data from the field of functional magnetic resonance imaging.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 2","pages":"191 - 216"},"PeriodicalIF":0.8,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143513289","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
Testing overidentifying restrictions on high-dimensional instruments and covariates 测试高维工具和协变量的过度识别限制
IF 0.8 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2024-12-05 DOI: 10.1007/s10463-024-00918-5
Hongwei Shi, Xinyu Zhang, Xu Guo, Baihua He, Chenyang Wang
{"title":"Testing overidentifying restrictions on high-dimensional instruments and covariates","authors":"Hongwei Shi,&nbsp;Xinyu Zhang,&nbsp;Xu Guo,&nbsp;Baihua He,&nbsp;Chenyang Wang","doi":"10.1007/s10463-024-00918-5","DOIUrl":"10.1007/s10463-024-00918-5","url":null,"abstract":"<div><p>The validity of instruments plays a crucial role in addressing endogenous treatment effects and instruments that violate the exclusion restriction are invalid. This paper concerns the overidentifying restrictions test for evaluating the validity of instruments in the high-dimensional instrumental variable model. We confront the challenge of high dimensionality by introducing a new testing procedure based on <i>U</i>-statistic. Our procedure allows the number of instruments and covariates to be in exponential order of the sample size. Under some mild conditions, we establish the asymptotic normality of the proposed test statistic under the null and local alternative hypotheses. The effectiveness of the proposed method is clearly supported by simulations and its application to a real dataset on trade and economic growth.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 2","pages":"331 - 352"},"PeriodicalIF":0.8,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143513249","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信