Annals of Statistics最新文献_第8页

Inference for Archimax copulas 阿基米德交配式的推论

IF 4.5 1区数学

Annals of Statistics Pub Date : 2020-04-01 DOI: 10.1214/19-aos1836

Simon Chatelain, Anne-Laure Fougères, J. Nešlehová

{"title":"Inference for Archimax copulas","authors":"Simon Chatelain, Anne-Laure Fougères, J. Nešlehová","doi":"10.1214/19-aos1836","DOIUrl":"https://doi.org/10.1214/19-aos1836","url":null,"abstract":"Archimax copula models can account for any type of asymptotic dependence between extremes and at the same time capture joint risks at medium levels. An Archimax copula is characterized by two functional parameters, the stable tail dependence function `, and the Archimedean generator ψ which distorts the extreme-value dependence structure. This article develops semiparametric inference for Archimax copulas: a nonparametric estimator of ` and a momentbased estimator of ψ assuming the latter belongs to a parametric family. Conditions under which ψ and ` are identifiable are derived. The asymptotic behavior of the estimators is then established under broad regularity conditions; performance in small samples is assessed through a comprehensive simulation study. The Archimax copula model with the Clayton generator is then used to analyze monthly rainfall maxima at three stations in French Brittany. The model is seen to fit the data very well, both in the lower and in the upper tail. The nonparametric estimator of ` reveals asymmetric extremal dependence between the stations, which reflects heavy precipitation patterns in the area. Technical proofs, simulation results and R code are provided in the Online Supplement.","PeriodicalId":8032,"journal":{"name":"Annals of Statistics","volume":"48 1","pages":"1025-1051"},"PeriodicalIF":4.5,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44067400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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: 9

Hurst function estimation 赫斯特函数估计

IF 4.5 1区数学

Annals of Statistics Pub Date : 2020-04-01 DOI: 10.1214/19-aos1825

Jinqi Shen, T. Hsing

引用次数: 1

Worst-case versus average-case design for estimation from partial pairwise comparisons 根据部分成对比较进行估计的最坏情况与平均情况设计

IF 4.5 1区数学

Annals of Statistics Pub Date : 2020-04-01 DOI: 10.1214/19-aos1838

A. Pananjady, Cheng Mao, Vidya Muthukumar, M. Wainwright, T. Courtade

{"title":"Worst-case versus average-case design for estimation from partial pairwise comparisons","authors":"A. Pananjady, Cheng Mao, Vidya Muthukumar, M. Wainwright, T. Courtade","doi":"10.1214/19-aos1838","DOIUrl":"https://doi.org/10.1214/19-aos1838","url":null,"abstract":"Pairwise comparison data arises in many domains, including tournament rankings, web search, and preference elicitation. Given noisy comparisons of a fixed subset of pairs of items, we study the problem of estimating the underlying comparison probabilities under the assumption of strong stochastic transitivity (SST). We also consider the noisy sorting subclass of the SST model. We show that when the assignment of items to the topology is arbitrary, these permutationbased models, unlike their parametric counterparts, do not admit consistent estimation for most comparison topologies used in practice. We then demonstrate that consistent estimation is possible when the assignment of items to the topology is randomized, thus establishing a dichotomy between worst-case and average-case designs. We propose two computationally efficient estimators in the average-case setting and analyze their risk, showing that it depends on the comparison topology only through the degree sequence of the topology. We also provide explicit classes of graphs for which the rates achieved by these estimators are optimal. Our results are corroborated by simulations on multiple comparison topologies.","PeriodicalId":8032,"journal":{"name":"Annals of Statistics","volume":"48 1","pages":"1072-1097"},"PeriodicalIF":4.5,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49323852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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: 9

Prediction error after model search 模型搜索后的预测错误

IF 4.5 1区数学

Annals of Statistics Pub Date : 2020-04-01 DOI: 10.1214/19-AOS1818

Xiaoying Tian

引用次数: 16

Bootstrap confidence regions based on M-estimators under nonstandard conditions 非标准条件下基于m估计量的自举置信区域

IF 4.5 1区数学

Annals of Statistics Pub Date : 2020-02-01 DOI: 10.1214/18-aos1803

Stephen M. S. Lee, Puyudi Yang

引用次数: 5

Two-step semiparametric empirical likelihood inference 两步半参数经验似然推理

IF 4.5 1区数学

Annals of Statistics Pub Date : 2020-02-01 DOI: 10.1214/18-AOS1788

Francesco Bravo, J. Escanciano, I. Keilegom

引用次数: 19

The multi-armed bandit problem: An efficient nonparametric solution 多武装土匪问题：一个有效的非参数解

IF 4.5 1区数学

Annals of Statistics Pub Date : 2020-02-01 DOI: 10.1214/19-aos1809

H. Chan

引用次数: 13

Penalized generalized empirical likelihood with a diverging number of general estimating equations for censored data 截尾数据广义估计方程具有发散数的惩罚广义经验似然

IF 4.5 1区数学

Annals of Statistics Pub Date : 2020-02-01 DOI: 10.1214/19-aos1870

Nian-Sheng Tang, Xiaodong Yan, Xingqiu Zhao

{"title":"Penalized generalized empirical likelihood with a diverging number of general estimating equations for censored data","authors":"Nian-Sheng Tang, Xiaodong Yan, Xingqiu Zhao","doi":"10.1214/19-aos1870","DOIUrl":"https://doi.org/10.1214/19-aos1870","url":null,"abstract":"This article considers simultaneous variable selection and parameter estimation as well as hypothesis testing in censored regression models with unspecified parametric likelihood. For the problem, we utilize certain growing dimensional general estimating equations and propose a penalized generalized empirical likelihood using the folded concave penalties. We first construct general estimating equations attaining the semiparametric efficiency bound with censored regression data and then establish the consistency and oracle properties of the penalized generalized empirical likelihood estimators. Furthermore, we show that the penalized generalized empirical likelihood ratio test statistic has an asymptotic standard central chi-squared distribution. The conditions of local and restricted global optimality of weighted penalized generalized empirical likelihood estimators are also discussed. We present an two-layer iterative algorithm for efficient implementation, and rigorously investigate its convergence property. The good performance of the proposed methods are demonstrated by extensive simulation studies and a real data example is provided for illustration.","PeriodicalId":8032,"journal":{"name":"Annals of Statistics","volume":"48 1","pages":"607-627"},"PeriodicalIF":4.5,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44061735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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: 3

Sparse SIR: Optimal rates and adaptive estimation 稀疏SIR:最优速率和自适应估计

IF 4.5 1区数学

Annals of Statistics Pub Date : 2020-02-01 DOI: 10.1214/18-aos1791

Kai Tan, Lei Shi, Zhou Yu

引用次数: 17

CONSISTENT SELECTION OF THE NUMBER OF CHANGE-POINTS VIA SAMPLE-SPLITTING. 通过样本分裂一致地选择改变点的数量。

IF 4.5 1区数学

Annals of Statistics Pub Date : 2020-02-01 Epub Date: 2020-02-17 DOI: 10.1214/19-aos1814

Changliang Zou, Guanghui Wang, Runze Li

{"title":"CONSISTENT SELECTION OF THE NUMBER OF CHANGE-POINTS VIA SAMPLE-SPLITTING.","authors":"Changliang Zou, Guanghui Wang, Runze Li","doi":"10.1214/19-aos1814","DOIUrl":"https://doi.org/10.1214/19-aos1814","url":null,"abstract":"<p><p>In multiple change-point analysis, one of the major challenges is to estimate the number of change-points. Most existing approaches attempt to minimize a Schwarz information criterion which balances a term quantifying model fit with a penalization term accounting for model complexity that increases with the number of change-points and limits overfitting. However, different penalization terms are required to adapt to different contexts of multiple change-point problems and the optimal penalization magnitude usually varies from the model and error distribution. We propose a data-driven selection criterion that is applicable to most kinds of popular change-point detection methods, including binary segmentation and optimal partitioning algorithms. The key idea is to select the number of change-points that minimizes the squared prediction error, which measures the fit of a specified model for a new sample. We develop a cross-validation estimation scheme based on an order-preserved sample-splitting strategy, and establish its asymptotic selection consistency under some mild conditions. Effectiveness of the proposed selection criterion is demonstrated on a variety of numerical experiments and real-data examples.</p>","PeriodicalId":8032,"journal":{"name":"Annals of Statistics","volume":"48 1","pages":"413-439"},"PeriodicalIF":4.5,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7397423/pdf/nihms-1022718.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38232848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28