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Sparse quantile regression via (ell _0)-penalty 通过(ell _0)惩罚稀疏分位数回归
IF 0.6 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2025-07-11 DOI: 10.1007/s10463-025-00941-0
Toshio Honda, Wei-Ying Wu
{"title":"Sparse quantile regression via (ell _0)-penalty","authors":"Toshio Honda,&nbsp;Wei-Ying Wu","doi":"10.1007/s10463-025-00941-0","DOIUrl":"10.1007/s10463-025-00941-0","url":null,"abstract":"<div><p>We consider model selection via <span>(ell _0)</span>-penalty for high-dimensional sparse quantile regression models. This procedure is almost equivalent to model selection via information criterion due to similarity in penalty. We deal with linear models, additive models, and varying coefficient models in a unified way and establish the model selection consistency results rigorously when the size of the relevant index set goes to infinity. The treatment of this situation is challenging and the theoretical novelty of our results is important because such information criteria are commonly used. We consider two different setups and propose tuning parameters in the <span>(ell _0)</span>-penalty. Besides, we propose a feasible algorithm for computation of our estimator and the numerical study results are presented.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"78 1","pages":"141 - 173"},"PeriodicalIF":0.6,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071353","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 greedy and optimistic clustering for leveraging individual covariate uncertainty 利用个体协变量不确定性的贪婪和乐观聚类
IF 0.6 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2025-07-11 DOI: 10.1007/s10463-025-00947-8
Akifumi Okuno, Kohei Hattori
{"title":"A greedy and optimistic clustering for leveraging individual covariate uncertainty","authors":"Akifumi Okuno,&nbsp;Kohei Hattori","doi":"10.1007/s10463-025-00947-8","DOIUrl":"10.1007/s10463-025-00947-8","url":null,"abstract":"<div><p>In this study, we examine a clustering problem where each individual element in a dataset has covariates associated with element-specific uncertainty. More specifically, we consider a clustering approach that preliminarily applies a non-linear transformation to the covariates, to capture the hidden data structure; we empirically approximate the sets representing the propagated uncertainty for the pre-processed features and propose a greedy and optimistic clustering (GOC) algorithm. This algorithm identifies better feature candidates within these sets, resulting in more condensed clusters. As a key application, we apply the GOC algorithm to synthetic datasets of the orbital properties of stars, generated through our numerical simulations that mimic the formation process of the Milky Way. The GOC algorithm demonstrates improved performance in identifying sibling stars originating from the same dwarf galaxy. These realistic datasets are also publicly available at https://github.com/oknakfm/GOC.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"78 2","pages":"275 - 296"},"PeriodicalIF":0.6,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147352756","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
Application of some (L_{2}) optimization to a discrete distribution 一些(L_{2})优化对离散分布的应用
IF 0.6 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2025-06-27 DOI: 10.1007/s10463-025-00933-0
Jiwoong Kim
{"title":"Application of some (L_{2}) optimization to a discrete distribution","authors":"Jiwoong Kim","doi":"10.1007/s10463-025-00933-0","DOIUrl":"10.1007/s10463-025-00933-0","url":null,"abstract":"<div><p>This paper proposes a novel method to estimate the success probability of the binomial distribution. The proposed method employs the Cramer-von Mises type optimization which has been commonly used in estimating parameters of continuous distributions. Upon obtaining the estimator through the proposed method, its desirable properties, such as asymptotic distribution and robustness, are rigorously investigated. Simulation studies serve to demonstrate that the proposed method compares favorably with other well-celebrated methods, including the maximum likelihood method.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"78 1","pages":"43 - 67"},"PeriodicalIF":0.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071377","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 variable selection in high-dimensional nonparametric additive model 高维非参数加性模型的鲁棒变量选择
IF 0.6 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2025-06-25 DOI: 10.1007/s10463-025-00939-8
Suneel Babu Chatla, Abhijit Mandal
{"title":"Robust variable selection in high-dimensional nonparametric additive model","authors":"Suneel Babu Chatla,&nbsp;Abhijit Mandal","doi":"10.1007/s10463-025-00939-8","DOIUrl":"10.1007/s10463-025-00939-8","url":null,"abstract":"<div><p>Additive models are flexible nonparametric models. Finding the nonzero additive components when the true model is assumed to be sparse is an important problem and is well studied. The existing research focused on using the <span>(L_2)</span> loss function, which is sensitive to outliers in the data. We propose a new variable selection method for additive models that is robust to outliers in the data. It considers the framework of B-splines and density power divergence loss function for estimation, and employs a nonconcave penalty for variable selection. Our asymptotic results are derived under the sub-Weibull assumption, which allows the error distribution to have an exponentially heavy tail. Under regularity conditions, we show that the proposed method achieves the optimal convergence rate. Our results include the convergence rates for sub-Gaussian and sub-Exponential distributions as special cases. We numerically validate the theoretical findings using simulations and real data analysis.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"78 1","pages":"115 - 140"},"PeriodicalIF":0.6,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071378","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
Posterior contraction rate and asymptotic Bayes optimality for one group global–local shrinkage priors in sparse normal means problem 稀疏正态均值问题中一组全局-局部收缩先验的后验收缩率和渐近贝叶斯最优性
IF 0.6 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2025-06-24 DOI: 10.1007/s10463-025-00932-1
Sayantan Paul, Arijit Chakrabarti
{"title":"Posterior contraction rate and asymptotic Bayes optimality for one group global–local shrinkage priors in sparse normal means problem","authors":"Sayantan Paul,&nbsp;Arijit Chakrabarti","doi":"10.1007/s10463-025-00932-1","DOIUrl":"10.1007/s10463-025-00932-1","url":null,"abstract":"<div><p>We study inference on the mean vector of the normal means model in sparse asymptotic settings when it is modelled by broad classes of one-group global–local continuous shrinkage priors. We prove that the resulting posterior distributions contract around the truth at a near minimax rate with respect to squared <span>(L_2)</span> loss when the global shrinkage parameter is estimated in empirical Bayesian ways or arbitrary priors supported on some appropriate interval are assigned to it. We then employ an intuitive multiple testing rule (using full Bayes treatment with global–local priors) in a problem of simultaneous testing (with additive misclassification loss) for the components of the mean assuming they are iid from a two-groups prior. In a first result of its kind, risk of our testing rule is shown to asymptotically match (up to a constant) that of the optimal rule in the two-groups setting. </p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 5","pages":"787 - 819"},"PeriodicalIF":0.6,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923235","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
Localization of moving poisson source on the plane 平面上运动泊松源的局部化
IF 0.6 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2025-06-16 DOI: 10.1007/s10463-025-00937-w
O. V. Chernoyarov, S. Dachian, Y. A. Kutoyants
{"title":"Localization of moving poisson source on the plane","authors":"O. V. Chernoyarov,&nbsp;S. Dachian,&nbsp;Y. A. Kutoyants","doi":"10.1007/s10463-025-00937-w","DOIUrl":"10.1007/s10463-025-00937-w","url":null,"abstract":"<div><p>Two problems of Poissonian source localization by <i>K</i> detectors on the plane are considered. It is supposed that the intensities of the received processes are decreasing functions of the distances between the source and the detectors. In the first problem the source is fixed at some point on the plane, and in the second problem it is supposed that the source is moving along some line. In both problems the properties of the least squares estimators, MLE, Bayesian estimators, One-step MLE and One-step MLE-processes are described. The regularity conditions are proposed which allow to prove the consistency and the asymptotic normality of all the estimators. Note that One-step MLE-processes allows <i>on-line</i> tracking of the moving source.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"78 1","pages":"69 - 92"},"PeriodicalIF":0.6,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071380","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
Selection-bias-adjusted inference for the bivariate normal distribution under soft-threshold sampling 软阈值抽样下二元正态分布的选择偏差调整推理
IF 0.6 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2025-05-02 DOI: 10.1007/s10463-025-00925-0
Joseph B. Lang
{"title":"Selection-bias-adjusted inference for the bivariate normal distribution under soft-threshold sampling","authors":"Joseph B. Lang","doi":"10.1007/s10463-025-00925-0","DOIUrl":"10.1007/s10463-025-00925-0","url":null,"abstract":"<div><p>The problem of estimating parameters and predicting outcomes of a bivariate Normal distribution is more challenging when, owing to data-dependent selection (or missingness or dropout), the available data are not a representative sample of bivariate realizations. This problem is addressed using an observation model that is induced by a combination of a multivariate Normal “science” model and a realistic “soft-threshold selection” model with unknown truncation point. This observation model, which is expressed using an intuitive selection subset notation, is a generalization of existing “hard-threshold” models. It affords simple-to-compute selection-bias-adjusted estimates of both the regression (conditional mean) parameters and the bivariate correlation. In addition, a simple bootstrap approach for computing both confidence and prediction intervals in the soft-threshold selection setting is described. Simulation results are promising. To motivate this research, two illustrative examples describe a setting where selection bias is an issue of concern.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 4","pages":"597 - 625"},"PeriodicalIF":0.6,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141921","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 signed-rank estimator for nonlinear regression models when covariates and errors are dependent 当协变量和误差相关时非线性回归模型的带符号秩估计
IF 0.6 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2025-04-30 DOI: 10.1007/s10463-025-00929-w
Hira L. Koul, Palaniappan Vellaisamy
{"title":"A signed-rank estimator for nonlinear regression models when covariates and errors are dependent","authors":"Hira L. Koul,&nbsp;Palaniappan Vellaisamy","doi":"10.1007/s10463-025-00929-w","DOIUrl":"10.1007/s10463-025-00929-w","url":null,"abstract":"<div><p>This paper contains the proof of the asymptotic uniform linearity of a sequence of simple linear signed-rank statistics based on the residuals of a class of nonlinear parametric regression models, where regression errors are possibly dependent on the covariates. This result in turn is used to prove the asymptotic normality of a signed rank estimator of the regression parameter vector in the given nonlinear regression model where covariates and regression errors are dependent and in the errors in variables linear regression model, when the distributions of the covariates and measurement errors are known.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 4","pages":"563 - 596"},"PeriodicalIF":0.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145145630","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
The family of multivariate beta copulas revisited 重新审视多元联结函数族
IF 0.6 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2025-04-28 DOI: 10.1007/s10463-025-00931-2
Enagnon Narcisse Agbangla, Jean-François Quessy, Louis-Paul Rivest
{"title":"The family of multivariate beta copulas revisited","authors":"Enagnon Narcisse Agbangla,&nbsp;Jean-François Quessy,&nbsp;Louis-Paul Rivest","doi":"10.1007/s10463-025-00931-2","DOIUrl":"10.1007/s10463-025-00931-2","url":null,"abstract":"<div><p>This article sheds new lights on the family of multivariate beta copulas that arises as the dependence structures of the multivariate generalized beta distribution of the second type. In particular, simple formulas for the computation of Kendall’s measure of association are derived and the asymmetry properties are investigated. Also, the multivariate extreme-value attractor of the beta copula is identified and it is shown that the beta family is closed under conditioning and belongs to the class of one-factor copulas. The sampling properties of the rank-based maximum-likelihood estimator are investigated with simulations and the usefulness of the beta copulas for the modeling of multivariate datasets is illustrated on triathlon data.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 5","pages":"757 - 786"},"PeriodicalIF":0.6,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923222","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
Offline minimax Q-function learning for undiscounted indefinite-horizon MDPs 未折现不定视界mdp的离线极大极小q函数学习
IF 0.6 4区 数学
Annals of the Institute of Statistical Mathematics Pub Date : 2025-04-21 DOI: 10.1007/s10463-025-00924-1
Fengying Li, Yuqiang Li, Xianyi Wu, Wei Bai
{"title":"Offline minimax Q-function learning for undiscounted indefinite-horizon MDPs","authors":"Fengying Li,&nbsp;Yuqiang Li,&nbsp;Xianyi Wu,&nbsp;Wei Bai","doi":"10.1007/s10463-025-00924-1","DOIUrl":"10.1007/s10463-025-00924-1","url":null,"abstract":"<div><p>This work considers the offline evaluation problem for indefinite-horizon Markov Decision Processes. A minimax Q-function learning algorithm is proposed, which, instead of i.i.d. tuples <span>((s,a,s',r))</span>, evaluates undiscounted expected return based by i.i.d. trajectories truncated at a given time step. The confidence error bounds are developed. Experiments using Open AI’s Cart Pole environment are employed to demonstrate the algorithm.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":"77 4","pages":"535 - 562"},"PeriodicalIF":0.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145144486","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
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