Biometrika最新文献_第6页

Graphical tools for selecting conditional instrumental sets 用于选择条件仪表组的图形工具

2区数学

Biometrika Pub Date : 2023-11-03 DOI: 10.1093/biomet/asad066

Henckel, Leonard, Buttenschön, Martin, Maathuis, Marloes H.

{"title":"Graphical tools for selecting conditional instrumental sets","authors":"Henckel, Leonard, Buttenschön, Martin, Maathuis, Marloes H.","doi":"10.1093/biomet/asad066","DOIUrl":"https://doi.org/10.1093/biomet/asad066","url":null,"abstract":"Summary We consider the efficient estimation of total causal effects in the presence of unmeasured confounding using conditional instrumental sets. Specifically, we consider the two-stage least squares estimator in the setting of a linear structural equation model with correlated errors that is compatible with a known acyclic directed mixed graph. To set the stage for our results, we characterize the class of linearly valid conditional instrumental sets that yield consistent two-stage least squares estimators for the target total effect and derive a new asymptotic variance formula for these estimators. Equipped with these results, we provide three graphical tools for selecting more efficient linearly valid conditional instrumental sets. First, a graphical criterion that for certain pairs of linearly valid conditional instrumental sets identifies which of the two corresponding estimators has the smaller asymptotic variance. Second, an algorithm that greedily adds covariates that reduce the asymptotic variance to a given linearly valid conditional instrumental set. Third, a linearly valid conditional instrumental set for which the corresponding estimator has the smallest asymptotic variance that can be ensured with a graphical criterion.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135775010","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

On inference in high-dimensional logistic regression models with separated data 分离数据的高维逻辑回归模型的推理

2区数学

Biometrika Pub Date : 2023-11-02 DOI: 10.1093/biomet/asad065

R M Lewis, H S Battey

{"title":"On inference in high-dimensional logistic regression models with separated data","authors":"R M Lewis, H S Battey","doi":"10.1093/biomet/asad065","DOIUrl":"https://doi.org/10.1093/biomet/asad065","url":null,"abstract":"Abstract Direct use of the likelihood function typically produces severely biased estimates when the dimension of the parameter vector is large relative to the effective sample size. With linearly separable data generated from a logistic regression model, the loglikelihood function asymptotes and the maximum likelihood estimator does not exist. We show that an exact analysis for each regression coefficient produces half-infinite confidence sets for some parameters when the data are separable. Such conclusions are not vacuous, but an honest portrayal of the limitations of the data. Finite confidence sets are only achievable when additional, perhaps implicit, assumptions are made. Under a notional double-asymptotic regime in which the dimension of the logistic coefficient vector increases with the sample size, the present paper considers the implications of enforcing a natural constraint on the vector of logistic-transformed probabilities. We derive a relationship between the logistic coefficients and a notional parameter obtained as a probability limit of an ordinary least squares estimator. The latter exists even when the data are separable. Consistency is ascertained under weak conditions on the design matrix.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135975796","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: 1

Nonparametric priors with full-range borrowing of information 具有全范围信息借用的非参数先验

2区数学

Biometrika Pub Date : 2023-10-19 DOI: 10.1093/biomet/asad063

F Ascolani, B Franzolini, A Lijoi, I Prünster

引用次数: 0

Likelihood-based Inference under Non-Convex Boundary Constraints 非凸边界约束下基于似然的推理

2区数学

Biometrika Pub Date : 2023-10-19 DOI: 10.1093/biomet/asad062

J Y Wang, Z S YE, Y Chen

引用次数: 0

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":null,"pages":null},"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":null,"pages":null},"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