Biometrika最新文献_第6页

On geometric convergence for MALA under simple conditions 简单条件下MALA的几何收敛性

2区数学

Biometrika Pub Date : 2023-10-03 DOI: 10.1093/biomet/asad060

Alain Oliviero-Durmus, Éric Moulines

引用次数: 0

Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning 离线强化学习中自然随机策略的有效评价

2区数学

Biometrika Pub Date : 2023-09-27 DOI: 10.1093/biomet/asad059

Nathan Kallus, Masatoshi Uehara

引用次数: 7

Selective machine learning of doubly robust functionals 双鲁棒函数的选择性机器学习

2区数学

Biometrika Pub Date : 2023-09-26 DOI: 10.1093/biomet/asad055

Y Cui, E Tchetgen Tchetgen

{"title":"Selective machine learning of doubly robust functionals","authors":"Y Cui, E Tchetgen Tchetgen","doi":"10.1093/biomet/asad055","DOIUrl":"https://doi.org/10.1093/biomet/asad055","url":null,"abstract":"Abstract While model selection is a well-studied topic in parametric and nonparametric regression or density estimation, selection of possibly high-dimensional nuisance parameters in semiparametric problems is far less developed. In this paper, we propose a selective machine learning framework for making inferences about a finite-dimensional functional defined on a semiparametric model, when the latter admits a doubly robust estimating function and several candidate machine learning algorithms are available for estimating the nuisance parameters. We introduce a new selection criterion aimed at bias reduction in estimating the functional of interest based on a novel definition of pseudo-risk inspired by the double robustness property. Intuitively, the proposed criterion selects a pair of learners with the smallest pseudo-risk, so that the estimated functional is least sensitive to perturbations of a nuisance parameter. We establish an oracle property for a multi-fold cross-validation version of the new selection criterion which states that our empirical criterion performs nearly as well as an oracle with a priori knowledge of the pseudo-risk for each pair of candidate learners. Finally, we apply the approach to model selection of a semiparametric estimator of average treatment effect given an ensemble of candidate machine learners to account for confounding in an observational study which we illustrate in simulations and a data application.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134960403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

An eigenvector-assisted estimation framework for signal-plus-noise matrix models 信号加噪声矩阵模型的特征向量辅助估计框架

2区数学

Biometrika Pub Date : 2023-09-19 DOI: 10.1093/biomet/asad058

Fangzheng Xie, Dingbo Wu

{"title":"An eigenvector-assisted estimation framework for signal-plus-noise matrix models","authors":"Fangzheng Xie, Dingbo Wu","doi":"10.1093/biomet/asad058","DOIUrl":"https://doi.org/10.1093/biomet/asad058","url":null,"abstract":"Summary In this paper, we develop an eigenvector-assisted estimation framework for a collection of signal-plus-noise matrix models arising in high-dimensional statistics and many applications. The framework is built upon a novel asymptotically unbiased estimating equation using the leading eigenvectors of the data matrix. However, the estimator obtained by directly solving the estimating equation could be numerically unstable in practice and lacks robustness against model misspecification. We propose to use the quasi-posterior distribution by exponentiating a criterion function whose maximizer coincides with the estimating equation estimator. The proposed framework can incorporate heteroskedastic variance information but does not require the complete specification of the sampling distribution and is also robust to the potential misspecification of the distribution of the noise matrix. Computationally, the quasi-posterior distribution can be obtained via a Markov Chain Monte Carlo sampler, which exhibits superior numerical stability than some of the existing optimization-based estimators and is straightforward for uncertainty quantification. Under mild regularity conditions, we establish the large sample properties of the quasi-posterior distributions. In particular, the quasi-posterior credible sets have the correct frequentist nominal coverage probability provided that the criterion function is carefully selected. The validity and usefulness of the proposed framework are demonstrated through the analysis of synthetic datasets and the real-world ENZYMES network datasets.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135060566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

E-values as unnormalized weights in multiple testing 在多重测试中，e值为非归一化权重

2区数学

Biometrika Pub Date : 2023-09-15 DOI: 10.1093/biomet/asad057

Nikolaos Ignatiadis, Ruodu Wang, Aaditya Ramdas

引用次数: 14

Retrospective causal inference with multiple effect variables 多效应变量的回顾性因果推理

2区数学

Biometrika Pub Date : 2023-09-14 DOI: 10.1093/biomet/asad056

Wei Li, Zitong Lu, Jinzhu Jia, Min Xie, Zhi Geng

引用次数: 0

Estimation of prediction error in time series 时间序列预测误差的估计

2区数学

Biometrika Pub Date : 2023-09-09 DOI: 10.1093/biomet/asad053

Alexander Aue, Prabir Burman

{"title":"Estimation of prediction error in time series","authors":"Alexander Aue, Prabir Burman","doi":"10.1093/biomet/asad053","DOIUrl":"https://doi.org/10.1093/biomet/asad053","url":null,"abstract":"Summary The accurate estimation of prediction errors in time series is an important problem, which has immediate implications for the accuracy of prediction intervals as well as the quality of a number of widely used time series model selection criteria such as the Akaike information criterion. Except for simple cases, however, it is difficult or even impossible to obtain exact analytical expressions for one-step and multi-step predictions. This may be one of the reasons that, unlike in the independent case (see Efron, 2004), up to now there has been no fully established methodology for time series prediction error estimation. Starting from an approximation to the bias-variance decomposition of the squared prediction error, a method for accurate estimation of prediction errors in both univariate and multivariate stationary time series is developed in this article. In particular, several estimates are derived for a general class of predictors that includes most of the popular linear, nonlinear, parametric and nonparametric time series models used in practice, with causal invertible autoregressive moving average and nonparametric autoregressive processes discussed as lead examples. Simulations demonstrate that the proposed estimators perform quite well in finite samples. The estimates may also be used for model selection when the purpose of modelling is prediction.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136108242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

More Efficient Exact Group Invariance Testing: using a Representative Subgroup 更有效的精确群不变性测试：使用代表子群

IF 2.7 2区数学

Biometrika Pub Date : 2023-09-01 DOI: 10.1093/biomet/asad050

N. W. Koning, J. Hemerik

引用次数: 0

Marginal proportional hazards models for multivariate interval-censored data. 多变量区间删失数据的边际比例危害模型。

IF 2.4 2区数学

Biometrika Pub Date : 2023-09-01 Epub Date: 2022-11-02 DOI: 10.1093/biomet/asac059

Yangjianchen Xu, Donglin Zeng, D Y Lin

{"title":"Marginal proportional hazards models for multivariate interval-censored data.","authors":"Yangjianchen Xu, Donglin Zeng, D Y Lin","doi":"10.1093/biomet/asac059","DOIUrl":"10.1093/biomet/asac059","url":null,"abstract":"<p><p>Multivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption that all event times are independent, and we provide a simple and stable EM-type algorithm. The resulting nonparametric maximum pseudolikelihood estimators for the regression parameters are shown to be consistent and asymptotically normal, with a limiting covariance matrix that can be consistently estimated by a sandwich estimator under arbitrary dependence structures for the related event times. We evaluate the performance of the proposed methods through extensive simulation studies and present an application to data from the Atherosclerosis Risk in Communities Study.</p>","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"110 3","pages":"815-830"},"PeriodicalIF":2.4,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10434824/pdf/nihms-1874830.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10490393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 0

ASSESSING TIME-VARYING CAUSAL EFFECT MODERATION IN THE PRESENCE OF CLUSTER-LEVEL TREATMENT EFFECT HETEROGENEITY AND INTERFERENCE. 在集群水平治疗效果异质性和干扰存在的情况下评估时变因果效应调节。

IF 2.7 2区数学

Biometrika Pub Date : 2023-09-01 DOI: 10.1093/biomet/asac065

Jieru Shi, Zhenke Wu, Walter Dempsey

引用次数: 2