The Annals of Statistics最新文献_第2页

Universal regression with adversarial responses 具有对抗性反应的普遍回归

The Annals of Statistics Pub Date : 2023-06-01 DOI: 10.1214/23-aos2299

Moise Blanchard, P. Jaillet

引用次数: 1

Minimax rate of distribution estimation on unknown submanifolds under adversarial losses 对抗性损失下未知子流形分布估计的极小极大率

The Annals of Statistics Pub Date : 2023-06-01 DOI: 10.1214/23-aos2291

Rong Tang, Yun Yang

引用次数: 0

AutoRegressive approximations to nonstationary time series with inference and applications 非平稳时间序列的自回归逼近及其推理与应用

The Annals of Statistics Pub Date : 2023-06-01 DOI: 10.1214/23-aos2288

Xiucai Ding, Zhou Zhou

引用次数: 0

Nonlinear independent component analysis for discrete-time and continuous-time signals 离散和连续信号的非线性独立分量分析

The Annals of Statistics Pub Date : 2023-04-01 DOI: 10.1214/23-aos2256

A. Schell, Harald Oberhauser

引用次数: 5

Breaking the winner’s curse in Mendelian randomization: Rerandomized inverse variance weighted estimator 打破孟德尔随机化中的赢家诅咒:再随机化逆方差加权估计器

The Annals of Statistics Pub Date : 2023-02-01 DOI: 10.1214/22-aos2247

Xinwei Ma, Jingshen Wang, Chong Wu

{"title":"Breaking the winner’s curse in Mendelian randomization: Rerandomized inverse variance weighted estimator","authors":"Xinwei Ma, Jingshen Wang, Chong Wu","doi":"10.1214/22-aos2247","DOIUrl":"https://doi.org/10.1214/22-aos2247","url":null,"abstract":"Developments in genome-wide association studies and the increasing availability of summary genetic association data have made the application of two-sample Mendelian Randomization (MR) with summary data increasingly popular. Conventional two-sample MR methods often employ the same sample for selecting relevant genetic variants and for constructing final causal estimates. Such a practice often leads to biased causal effect estimates due to the well known\"winner's curse\"phenomenon. To address this fundamental challenge, we first examine its consequence on causal effect estimation both theoretically and empirically. We then propose a novel framework that systematically breaks the winner's curse, leading to unbiased association effect estimates for the selected genetic variants. Building upon the proposed framework, we introduce a novel rerandomized inverse variance weighted estimator that is consistent when selection and parameter estimation are conducted on the same sample. Under appropriate conditions, we show that the proposed RIVW estimator for the causal effect converges to a normal distribution asymptotically and its variance can be well estimated. We illustrate the finite-sample performance of our approach through Monte Carlo experiments and two empirical examples.","PeriodicalId":22375,"journal":{"name":"The Annals of Statistics","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86399291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Sharp global convergence guarantees for iterative nonconvex optimization with random data 全局收敛性保证了随机数据下迭代非凸优化

The Annals of Statistics Pub Date : 2023-02-01 DOI: 10.1214/22-aos2246

Kabir Chandrasekher, Ashwin Pananjady, Christos Thrampoulidis

{"title":"Sharp global convergence guarantees for iterative nonconvex optimization with random data","authors":"Kabir Chandrasekher, Ashwin Pananjady, Christos Thrampoulidis","doi":"10.1214/22-aos2246","DOIUrl":"https://doi.org/10.1214/22-aos2246","url":null,"abstract":"We consider a general class of regression models with normally distributed covariates, and the associated nonconvex problem of ﬁtting these models from data. We develop a general recipe for analyzing the convergence of iterative algorithms for this task from a random initialization. In particular, provided each iteration can be written as the solution to a convex optimization problem satisfying some natural conditions, we leverage Gaussian comparison theorems to derive a deterministic sequence that provides sharp upper and lower bounds on the error of the algorithm with sample-splitting. Crucially, this deterministic sequence accurately captures both the convergence rate of the algorithm and the eventual error ﬂoor in the ﬁnite-sample regime, and is distinct from the commonly used “population” sequence that results from taking the inﬁnite-sample limit. We apply our general framework to derive several concrete consequences for parameter estimation in popular statistical models including phase retrieval and mixtures of regressions. Provided the sample size scales near-linearly in the dimension, we show sharp global convergence rates for both higher-order algorithms based on alternating updates and ﬁrst-order algorithms based on subgradient descent. These corollaries, in turn, reveal multiple nonstandard phenomena that are then corroborated by extensive numerical experiments.","PeriodicalId":22375,"journal":{"name":"The Annals of Statistics","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80800901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Sup-norm adaptive drift estimation for multivariate nonreversible diffusions 多元不可逆扩散的超范数自适应漂移估计

The Annals of Statistics Pub Date : 2022-12-01 DOI: 10.1214/22-aos2237

Cathrine Aeckerle-Willems, C. Strauch

{"title":"Sup-norm adaptive drift estimation for multivariate nonreversible diffusions","authors":"Cathrine Aeckerle-Willems, C. Strauch","doi":"10.1214/22-aos2237","DOIUrl":"https://doi.org/10.1214/22-aos2237","url":null,"abstract":"We consider the question of estimating the drift for a large class of ergodic multivariate and possibly nonreversible diffusion processes, based on continuous observations, in sup -norm loss. Nonparametric classes of smooth functions of unknown order are considered, and we suggest an adaptive approach which allows to construct drift estimators attaining optimal sup -norm rates of convergence. Reversibility structures and related functional inequalities are known to be key tools for these estimation problems. We can discard such restrictions by making use of mixing properties which are satisﬁed for the very general class of processes under consideration. Analysing diffusions, the scalar case is very distinct from the general multivariate setting. Therefore, we treat scalar and multivariate processes separately which leads to in several aspects improved univariate results. While we consider drift estimation on bounded domains for exponentially β -mixing multivariate processes, for scalar diffusion processes we work under minimal assumptions that allow estimation of unbounded drift terms over the entire real line, and we provide classical minimax results (including lower bounds) which cannot be obtained under state-of-the-art conditions in the multivariate case. In addition, we prove a Donsker theorem for the classical kernel estimator of the invariant density in the scalar setting and establish its semiparametric efﬁciency.","PeriodicalId":22375,"journal":{"name":"The Annals of Statistics","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87453902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Choosing between persistent and stationary volatility 在持续波动和平稳波动之间进行选择

The Annals of Statistics Pub Date : 2022-12-01 DOI: 10.1214/22-aos2236

Ilias Chronopoulos, L. Giraitis, G. Kapetanios

引用次数: 1

Coverage of credible intervals in Bayesian multivariate isotonic regression 贝叶斯多元等渗回归中可信区间的覆盖

The Annals of Statistics Pub Date : 2022-11-22 DOI: 10.1214/23-aos2298

Kangkang Wang, S. Ghosal

{"title":"Coverage of credible intervals in Bayesian multivariate isotonic regression","authors":"Kangkang Wang, S. Ghosal","doi":"10.1214/23-aos2298","DOIUrl":"https://doi.org/10.1214/23-aos2298","url":null,"abstract":"We consider the nonparametric multivariate isotonic regression problem, where the regression function is assumed to be nondecreasing with respect to each predictor. Our goal is to construct a Bayesian credible interval for the function value at a given interior point with assured limiting frequentist coverage. We put a prior on unrestricted step-functions, but make inference using the induced posterior measure by an\"immersion map\"from the space of unrestricted functions to that of multivariate monotone functions. This allows maintaining the natural conjugacy for posterior sampling. A natural immersion map to use is a projection via a distance, but in the present context, a block isotonization map is found to be more useful. The approach of using the induced\"immersion posterior\"measure instead of the original posterior to make inference provides a useful extension of the Bayesian paradigm, particularly helpful when the model space is restricted by some complex relations. We establish a key weak convergence result for the posterior distribution of the function at a point in terms of some functional of a multi-indexed Gaussian process that leads to an expression for the limiting coverage of the Bayesian credible interval. Analogous to a recent result for univariate monotone functions, we find that the limiting coverage is slightly higher than the credibility, the opposite of a phenomenon observed in smoothing problems. Interestingly, the relation between credibility and limiting coverage does not involve any unknown parameter. Hence by a recalibration procedure, we can get a predetermined asymptotic coverage by choosing a suitable credibility level smaller than the targeted coverage, and thus also shorten the credible intervals.","PeriodicalId":22375,"journal":{"name":"The Annals of Statistics","volume":"152 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77051651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Optimal Permutation Estimation in CrowdSourcing problems 众包问题中的最优排列估计

The Annals of Statistics Pub Date : 2022-11-08 DOI: 10.1214/23-aos2271

Emmanuel Pilliat, A. Carpentier, N. Verzelen

引用次数: 2