BIOMETRIKA 主要是一本统计学杂志，其中重点放在包含在应用中具有直接或潜在价值的原始理论贡献的论文上。不时还会发表一些边缘领域的论文。

Biometrika is a journal primarily focused on statistics, particularly papers that present original theoretical contributions with direct or potential value in applications. Occasionally, it also publishes research from peripheral areas.

《Biometrika》是一本主要关注统计学的期刊，特别是那些在应用中具有直接或潜在价值的原创理论贡献的论文。此外，期刊偶尔也会发表一些边缘领域的研究成果。

Biometrika

Abstract : The results of observational studies are often disputed because of nonrandom treatment assignment. For example, patients at greater risk may be overrepresented in some treatment group. This paper discusses the central role of propensity scores and balancing scores in the analysis of observational studies. The propensity score is the (estimated) conditional probability of assignment to a particular treatment given a vector of observed covariates. Both large and small sample theory show that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates. Applications include: matched sampling on the univariate propensity score which is equal percent bias reducing under more general conditions than required for discriminant matching, multivariate adjustment by subclassification on balancing scores where the same subclasses are used to estimate treatment effects for all outcome variables and in all subpopulations, and visual representation of multivariate adjustment by a two-dimensional plot. (Author)

PAUL R. ROSENBAUM

DONALD B. RUBIN

The central role of the propensity score in observational studies for causal effects

倾向评分在因果效应观察性研究中的核心作用

The study of stochastic processes has naturally led to the consideration of stochastic phenomena which are distributed in space of two or more dimensions. Such investigations are, for instance, of practical interest in connexion with problems concerning the distribution of soil fertility over a field or the relations between the velocities at different points in a turbulent fluid. A review of such work with many references has recently been given by Ghosh (1949) (see also Matern, 1947). In the present note I consider two problems arising in the twoand three-dimensional cases.

P. A. P. MORAN

NOTES ON CONTINUOUS STOCHASTIC PHENOMENA

连续随机现象札记

Journal Article A Wilcoxon–Mann–Whitney-type test for infinite-dimensional data Get access Anirvan Chakraborty, Anirvan Chakraborty Theoretical Statistics and Mathematics Unit, Indian Statistical Institute, 203 Barrackpore Trunk Road, Kolkata 700108, India, vanchak@gmail.comprobal@isical.ac.in Search for other works by this author on: Oxford Academic Google Scholar Probal Chaudhuri Probal Chaudhuri Theoretical Statistics and Mathematics Unit, Indian Statistical Institute, 203 Barrackpore Trunk Road, Kolkata 700108, India, vanchak@gmail.comprobal@isical.ac.in Search for other works by this author on: Oxford Academic Google Scholar Biometrika, Volume 102, Issue 1, March 2015, Pages 239–246, https://doi.org/10.1093/biomet/asu072 Published: 09 February 2015 Article history Received: 01 July 2013 Revision received: 01 November 2014 Published: 09 February 2015

A. Chakraborty

P. Chaudhuri

A Wilcoxon-Mann-Whitney-type test for infinite-dimensional data

无穷维数据的Wilcoxon-Mann-Whitney型检验

Summary We propose a model-free variable screening method for the optimal treatment regime with high-dimensional survival data. The proposed screening method provides a unified framework to select the active variables in a prespecified target population, including the treated group as a special case. Based on this framework, the optimal treatment regime is exactly the optimal classifier that minimizes a weighted misclassification error rate, with weights associated with survival outcome variables, the censoring distribution and a prespecified target population. Our main contribution involves reformulating the weighted classification problem into a classification problem within a hypothetical population, where the observed data can be viewed as a sample obtained from outcome-dependent sampling, with the selection probability inversely proportional to the weights. Consequently, we introduce the weighted Kolmogorov–Smirnov approach for selecting active variables in the optimal treatment regime, extending the conventional Kolmogorov–Smirnov method for binary classification. Additionally, the proposed screening method exhibits two levels of robustness. The first level of robustness is achieved because the proposed method does not require any model assumptions for the survival outcome on treatment and covariates, whereas the other is attained as the form of treatment regimes is allowed to be unspecified even without requiring convex surrogate loss, such as logit loss or hinge loss. As a result, the proposed screening method is robust to model misspecifications, and nonparametric learning methods such as random forests and boosting can be applied to those selected variables for further analysis. The theoretical properties of the proposed method are established. The performance of the proposed method is examined through simulation studies and illustrated by a lung cancer dataset.

Cheng-Han Yang

Yu-Jen Cheng

A model-free variable screening method for optimal treatment regimes with high-dimensional survival data

无模型变量筛选方法在高维生存数据最优治疗方案中的应用

Journal Article THE GENERALISED PRODUCT MOMENT DISTRIBUTION IN SAMPLES FROM A NORMAL MULTIVARIATE POPULATION Get access JOHN WISHART, M.A., B.Sc JOHN WISHART, M.A., B.Sc Statistical DepartmentRothamsted Experimental Station Search for other works by this author on: Oxford Academic Google Scholar Biometrika, Volume 20A, Issue 1-2, December 1928, Pages 32–52, https://doi.org/10.1093/biomet/20A.1-2.32 Published: 01 December 1928

John Wishart

THE GENERALISED PRODUCT MOMENT DISTRIBUTION IN SAMPLES FROM A NORMAL MULTIVARIATE POPULATION

正态多元总体样本中的广义积矩分布

Abstract Local variable selection aims to test for the effect of covariates on an outcome within specific regions. We outline a challenge that arises in the presence of nonlinear effects and model misspecification. Specifically, for common semiparametric methods even slight model misspecification can result in a high false positive rate, in a manner that is highly sensitive to the chosen basis functions. We propose a methodology based on orthogonal cut splines that avoids false positive inflation for any choice of knots, and achieves consistent local variable selection. Our approach offers simplicity, handles both continuous and categorical covariates, and provides theory for high-dimensional covariates and model misspecification. We discuss settings with either independent or dependent data. Our proposal allows including adjustment covariates that do not undergo selection, enhancing the model’s flexibility. Our examples describe salary gaps associated with various discrimination factors at different ages, and the effects of covariates on functional data measuring brain activation at different times.

D Rossell

A K Seong

I Saez

M Guindani

Semi-parametric local variable selection under misspecification

误设条件下的半参数局部变量选择

Abstract This paper addresses the asymptotic performance of popular spatial regression estimators of the linear effect of an exposure on an outcome under spatial confounding, the presence of an unmeasured spatially-structured variable influencing both the exposure and the outcome. We first show that the estimators from ordinary least squares and restricted spatial regression are asymptotically biased under spatial confounding. We then prove a novel main result on the in-fill consistency of the generalized least squares estimator using a working covariance matrix from a Matérn or square exponential kernel, in the presence of spatial confounding. The result holds under very mild assumptions, accommodating any exposure with some nonspatial variation, any spatially continuous fixed confounder function, and non-Gaussian errors in both the exposure and outcome. Finally, we prove that spatial estimators from generalized least squares, Gaussian process regression, and spline models that are consistent under confounding by a fixed function will also be consistent under endogeneity or confounding by a random function, ie, a stochastic process. We conclude that, contrary to claims in some literature on spatial confounding, traditional spatial estimators are capable of estimating linear exposure effects under spatial confounding as long as there is some noise in the exposure. We support our theoretical arguments with simulation studies.

Brian Gilbert

Elizabeth L Ogburn

Abhirup Datta

Consistency of common spatial estimators under spatial confounding

空间混杂下常见空间估计量的一致性

SUMMARY In recent years, real-world external controls have grown in popularity as a tool to empower randomized placebo-controlled trials, particularly in rare diseases or cases where balanced randomization is unethical or impractical. However, as external controls are not always comparable to the trials, direct borrowing without scrutiny may heavily bias the treatment effect estimator. Our paper proposes a data-adaptive integrative framework capable of preventing unknown biases of the external controls. The adaptive nature is achieved by dynamically sorting out a comparable subset of the external controls via bias penalization. Our proposed method can simultaneously achieve (a) the semiparametric efficiency bound when the external controls are comparable and (b) selective borrowing that mitigates the impact of the existence of incomparable external controls. Furthermore, we establish statistical guarantees, including consistency, asymptotic distribution, and inference, providing type-I error control and good power. Extensive simulations and two real-data applications show that the proposed method leads to improved performance over the trial-only estimator across various bias-generating scenarios.

Chenyin Gao

Shu Yang

Mingyang Shan

Wenyu Y E

Ilya Lipkovich

Douglas Faries

Improving randomized controlled trial analysis via data-adaptive borrowing

通过数据自适应借用改进随机对照试验分析

Abstract Distinguishing two candidate models is a fundamental and practically important statistical problem. Error rate control is crucial to the testing logic but, in complex nonparametric settings, can be difficult to achieve, especially when the stopping rule that determines the data collection process is not available. This paper proposes an e-process construction based on the predictive recursion algorithm originally designed to recursively fit nonparametric mixture models. The resulting predictive recursion process affords anytime valid inference and is asymptotically efficient in the sense that its growth rate is first-order optimal relative to the predictive recursion’s mixture model.

Vaidehi Dixit

Ryan Martin

Anytime valid and asymptotically efficient inference driven by predictive recursion

基于预测递归的随时有效且渐近高效的推断

Abstract While generalized linear mixed models are a fundamental tool in applied statistics, many specifications, such as those involving categorical factors with many levels or interaction terms, can be computationally challenging to estimate due to the need to compute or approximate high-dimensional integrals. Variational inference is a popular way to perform such computations, especially in the Bayesian context. However, naive use of such methods can provide unreliable uncertainty quantification. We show that this is indeed the case for mixed models, proving that standard mean-field variational inference dramatically underestimates posterior uncertainty in high-dimensions. We then show how appropriately relaxing the mean-field assumption leads to methods whose uncertainty quantification does not deteriorate in high-dimensions, and whose total computational cost scales linearly with the number of parameters and observations. Our theoretical and numerical results focus on mixed models with Gaussian or binomial likelihoods, and rely on connections to random graph theory to obtain sharp high-dimensional asymptotic analysis. We also provide generic results, which are of independent interest, relating the accuracy of variational inference to the convergence rate of the corresponding coordinate ascent algorithm that is used to find it. Our proposed methodology is implemented in the R package vglmer. Numerical results with simulated and real data examples illustrate the favourable computation cost versus accuracy trade-off of our approach compared to various alternatives.

Abbreviation
Journal Impact	2.51
Quartiles(Global)	STATISTICS & PROBABILITY(Q1)

ISSN	0006-3444, 1464-3510
h-index	132

Publication Information	Publisher: Oxford University Press，Publishing cycle: Quarterly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1965，Proportion of original research papers: 100.00%，Self Citation Rate:4.20%， Gold OA Rate: 14.67%
Average review cycle	网友分享经验：较慢,6-12周
Average recruitment ratio	网友分享经验：较易

Biometrika

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