Annals of the Institute of Statistical Mathematics最新文献_第7页

Inhomogeneous hidden semi-Markov models for incompletely observed point processes 不完全观测点过程的非齐次隐半马尔可夫模型

IF 1 4区数学

Annals of the Institute of Statistical Mathematics Pub Date : 2022-09-18 DOI: 10.1007/s10463-022-00843-5

Amina Shahzadi, Ting Wang, Mark Bebbington, Matthew Parry

引用次数: 1

Regression analysis for exponential family data in a finite population setup using two-stage cluster sample 有限总体条件下指数族数据的两阶段聚类回归分析

IF 1 4区数学

Annals of the Institute of Statistical Mathematics Pub Date : 2022-09-14 DOI: 10.1007/s10463-022-00850-6

Brajendra C. Sutradhar

引用次数: 0

Asymptotic theory in network models with covariates and a growing number of node parameters 具有协变和增长节点参数的网络模型的渐近理论

IF 1 4区数学

Annals of the Institute of Statistical Mathematics Pub Date : 2022-09-02 DOI: 10.1007/s10463-022-00848-0

Qiuping Wang, Yuan Zhang, Ting Yan

引用次数: 1

Quantitative robustness of instance ranking problems 实例排序问题的定量鲁棒性

IF 1 4区数学

Annals of the Institute of Statistical Mathematics Pub Date : 2022-08-30 DOI: 10.1007/s10463-022-00847-1

Tino Werner

引用次数: 2

Forward variable selection for ultra-high dimensional quantile regression models 超高维分位数回归模型的正向变量选择

IF 1 4区数学

Annals of the Institute of Statistical Mathematics Pub Date : 2022-08-29 DOI: 10.1007/s10463-022-00849-z

Toshio Honda, Chien-Tong Lin

引用次数: 1

Conditional selective inference for robust regression and outlier detection using piecewise-linear homotopy continuation 稳健回归的条件选择推理和分段线性同伦延拓的离群值检测

IF 1 4区数学

Annals of the Institute of Statistical Mathematics Pub Date : 2022-08-27 DOI: 10.1007/s10463-022-00846-2

Toshiaki Tsukurimichi, Yu Inatsu, Vo Nguyen Le Duy, Ichiro Takeuchi

引用次数: 11

Flexible asymmetric multivariate distributions based on two-piece univariate distributions 基于两件式单变量分布的柔性非对称多变量分布

IF 1 4区数学

Annals of the Institute of Statistical Mathematics Pub Date : 2022-08-02 DOI: 10.1007/s10463-022-00842-6

Jonas Baillien, Irène Gijbels, Anneleen Verhasselt

{"title":"Flexible asymmetric multivariate distributions based on two-piece univariate distributions","authors":"Jonas Baillien, Irène Gijbels, Anneleen Verhasselt","doi":"10.1007/s10463-022-00842-6","DOIUrl":"10.1007/s10463-022-00842-6","url":null,"abstract":"<div><p>Classical symmetric distributions like the Gaussian are widely used. However, in reality data often display a lack of symmetry. Multiple distributions, grouped under the name “skewed distributions”, have been developed to specifically cope with asymmetric data. In this paper, we present a broad family of flexible multivariate skewed distributions for which statistical inference is a feasible task. The studied family of multivariate skewed distributions is derived by taking affine combinations of independent univariate distributions. These are members of a flexible family of univariate asymmetric distributions and are an important basis for achieving statistical inference. Besides basic properties of the proposed distributions, also statistical inference based on a maximum likelihood approach is presented. We show that under mild conditions, weak consistency and asymptotic normality of the maximum likelihood estimators hold. These results are supported by a simulation study confirming the developed theoretical results, and some data examples to illustrate practical applicability.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48406413","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 the choice of the optimal single order statistic in quantile estimation 分位数估计中最优单阶统计量的选择

IF 1 4区数学

Annals of the Institute of Statistical Mathematics Pub Date : 2022-08-02 DOI: 10.1007/s10463-022-00845-3

Mariusz Bieniek, Luiza Pańczyk

引用次数: 0

Selective inference after feature selection via multiscale bootstrap 多尺度自举特征选择后的选择性推理

IF 1 4区数学

Annals of the Institute of Statistical Mathematics Pub Date : 2022-07-30 DOI: 10.1007/s10463-022-00838-2

Yoshikazu Terada, Hidetoshi Shimodaira

{"title":"Selective inference after feature selection via multiscale bootstrap","authors":"Yoshikazu Terada, Hidetoshi Shimodaira","doi":"10.1007/s10463-022-00838-2","DOIUrl":"10.1007/s10463-022-00838-2","url":null,"abstract":"<div><p>It is common to show the confidence intervals or <i>p</i>-values of selected features, or predictor variables in regression, but they often involve selection bias. The selective inference approach solves this bias by conditioning on the selection event. Most existing studies of selective inference consider a specific algorithm, such as Lasso, for feature selection, and thus they have difficulties in handling more complicated algorithms. Moreover, existing studies often consider unnecessarily restrictive events, leading to over-conditioning and lower statistical power. Our novel and widely applicable resampling method via multiscale bootstrap addresses these issues to compute an approximately unbiased selective <i>p</i>-value for the selected features. As a simplification of the proposed method, we also develop a simpler method via the classical bootstrap. We prove that the <i>p</i>-value computed by our multiscale bootstrap method is more accurate than the classical bootstrap method. Furthermore, numerical experiments demonstrate that our algorithm works well even for more complicated feature selection methods such as non-convex regularization.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43509814","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: 2

Inference using an exact distribution of test statistic for random-effects meta-analysis 随机效应荟萃分析使用检验统计量的精确分布进行推断

IF 1 4区数学

Annals of the Institute of Statistical Mathematics Pub Date : 2022-07-26 DOI: 10.1007/s10463-022-00844-4

Keisuke Hanada, Tomoyuki Sugimoto

{"title":"Inference using an exact distribution of test statistic for random-effects meta-analysis","authors":"Keisuke Hanada, Tomoyuki Sugimoto","doi":"10.1007/s10463-022-00844-4","DOIUrl":"10.1007/s10463-022-00844-4","url":null,"abstract":"<div><p>Random-effects meta-analysis serves to integrate the results of multiple studies with methods such as moment estimation and likelihood estimation duly proposed. These existing methods are based on asymptotic normality with respect to the number of studies. However, the test and interval estimation deviate from the nominal significance level when integrating a small number of studies. Although a method for constructing more conservative intervals has been recently proposed, the exact distribution of test statistic for the overall treatment effect is not well known. In this paper, we provide an almost-exact distribution of the test statistic in random-effects meta-analysis and propose the test and interval estimation using the almost-exact distribution. Simulations demonstrate the accuracy of estimation and application to existing meta-analysis using the method proposed here. With known variance parameters, the estimation performance using the almost-exact distribution always achieves the nominal significance level regardless of the number of studies and heterogeneity. We also propose some methods to construct a conservative interval estimation, even when the variance parameters are unknown, and present their performances via simulation and an application to Alzheimer’s disease meta-analysis.</p></div>","PeriodicalId":55511,"journal":{"name":"Annals of the Institute of Statistical Mathematics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41358458","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