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

Journal Article Variable selection in high-dimensional linear models: partially faithful distributions and the pc-simple algorithm Get access P. Bühlmann, P. Bühlmann Seminar für Statistik, Department of Mathematics, ETH Zurich, Rämistrasse 101, 8092 Zurich, Switzerlandbuhlmann@stat.math.ethz.chkalisch@stat.math.ethz.chmaathuis@stat.math.ethz.ch Search for other works by this author on: Oxford Academic Google Scholar M. Kalisch, M. Kalisch Seminar für Statistik, Department of Mathematics, ETH Zurich, Rämistrasse 101, 8092 Zurich, Switzerlandbuhlmann@stat.math.ethz.chkalisch@stat.math.ethz.chmaathuis@stat.math.ethz.ch Search for other works by this author on: Oxford Academic Google Scholar M. H. Maathuis M. H. Maathuis Seminar für Statistik, Department of Mathematics, ETH Zurich, Rämistrasse 101, 8092 Zurich, Switzerlandbuhlmann@stat.math.ethz.chkalisch@stat.math.ethz.chmaathuis@stat.math.ethz.ch Search for other works by this author on: Oxford Academic Google Scholar Biometrika, Volume 97, Issue 2, June 2010, Pages 261–278, https://doi.org/10.1093/biomet/asq008 Published: 30 April 2010 Article history Received: 01 August 2008 Revision received: 01 October 2009 Published: 30 April 2010

P. Buhlmann

M. Kalisch

M. H. Maathuis

Variable selection in high-dimensional linear models: partially faithful distributions and the PC-simple algorithm

高维线性模型中的变量选择：部分忠实分布与PC-简单算法

Journal Article Extended Bayesian information criteria for model selection with large model spaces Get access Jiahua Chen, Jiahua Chen Department of Statistics, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canadajhchen@stat.ubc.ca Search for other works by this author on: Oxford Academic Google Scholar Zehua Chen Zehua Chen Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546stachenz@nus.edu.sg Search for other works by this author on: Oxford Academic Google Scholar Biometrika, Volume 95, Issue 3, September 2008, Pages 759–771, https://doi.org/10.1093/biomet/asn034 Published: 01 September 2008 Article history Received: 01 January 2007 Revision received: 01 January 2008 Published: 01 September 2008

J. Chen

Z. Chen

Extended Bayesian information criteria for model selection with large model spaces

扩展贝叶斯信息准则在大模型空间中的模型选择

M G KENDALL

The treatment of ties in ranking problems.

排名问题中并列的处理方法。

Monte Carlo sampling methods using Markov chains and their applications Get access W. K. Hastings W. K. Hastings University of Toronto Search for other works by this author on: Oxford Academic Google Scholar Biometrika, Volume 57, Issue 1, April 1970, Pages 97–109, https://doi.org/10.1093/biomet/57.1.97 Published: 01 April 1970 Article history Received: 01 February 1969 Revision received: 01 July 1969 Published: 01 April 1970

W. K. Hastings

Monte Carlo sampling methods using Markov chains and their applications

使用马尔可夫链的蒙特卡罗抽样方法及其应用

Abstract The generalization ability of kernel interpolation in large dimensions, i.e., 𝑛 ≍ 𝑑𝛾 for some 𝛾 &amp;gt; 0, might be one of the most interesting problems in the recent renaissance of kernel regression, since it may help us understand the ‘benign overfitting phenomenon’ reported in the neural networks literature. Focusing on the inner product kernel on the unit sphere, we fully characterize the exact order of both the variance and bias of large-dimensional kernel interpolation under various source conditions 𝑠 ≥ 0. Consequently, we obtain the (𝑠, 𝛾)-phase diagram of large-dimensional kernel interpolation, i.e., we determine the regions in the (𝑠, 𝛾)-plane where the kernel interpolation is minimax optimal, sub-optimal and inconsistent.

Haobo Zhang

Weihao Lu

Qian Lin

The phase diagram of kernel interpolation in large dimensions

大维核插值的相图

SUMMARY Model-based clustering of moderate or large dimensional data is notoriously difficult. We propose a model for simultaneous dimensionality reduction and clustering by assuming a mixture model for a set of latent scores, which are then linked to the observations via a Gaussian latent factor model. This approach was recently investigated by Chandra et al. (2023). The authors use a factor-analytic representation and assume a mixture model for the latent factors. However, performance can deteriorate in the presence of model misspecification. Assuming a repulsive point process prior for the component-specific means of the mixture for the latent scores is shown to yield a more robust model that outperforms the standard mixture model for the latent factors in several simulated scenarios. The repulsive point process must be anisotropic to favour well-separated clusters of data, and its density should be tractable for efficient posterior inference. We address these issues by proposing a general construction for anisotropic determinantal point processes. We illustrate our model in simulations as well as a plant species co-occurrence dataset.

L Ghilotti

M Beraha

A Guglielmi

Bayesian clustering of high-dimensional data via latent repulsive mixtures

通过潜在排斥混合对高维数据进行贝叶斯聚类

SUMMARY Given a collection of features available for inclusion in a predictive model, it may be of interest to quantify the relative importance of a subset of features for the prediction task at hand. For example, in HIV vaccine trials, participant baseline characteristics are used to predict the probability of HIV acquisition over the intended follow-up period, and investigators may wish to understand how much certain types of predictors, such as behavioral factors, contribute toward overall predictiveness. Time-to-event outcomes such as time to HIV acquisition are often subject to right censoring, and existing methods for assessing variable importance are typically not intended to be used in this setting. We describe a broad class of algorithm-agnostic variable importance measures for prediction in the context of survival data. We propose a nonparametric efficient estimation procedure that incorporates flexible learning of nuisance parameters, yields asymptotically valid inference, and enjoys double-robustness. We assess the performance of our proposed procedure via numerical simulations and analyze data from the HVTN 702 vaccine trial to inform enrollment strategies for future HIV vaccine trials.

C J Wolock

P B Gilbert

N Simon

M Carone

Assessing variable importance in survival analysis using machine learning

评估机器学习在生存分析中变量重要性的研究

Abstract This study addresses the challenge of distribution estimation and inference in a semi-supervised setting. In contrast to prior research focusing on parameter inference, this work explores the complexities of semi-supervised distribution estimation, particularly the uniformity problem inherent in functional processes. To tackle this issue, we introduce a versatile framework designed to extract valuable information from unlabelled data by approximating a conditional distribution on covariates. The proposed estimator is derived from the K-fold cross-fitting strategy, exhibiting both consistency and asymptotic Gaussian process properties. Under mild conditions, the proposed estimator outperforms the empirical cumulative distribution function in terms of asymptotic efficiency. Several applications of the methodology are given, including parameter inference and goodness-of-fit tests.

Mengtao Wen

Yinxu Jia

Haojie Ren

Zhaojun Wang

Changliang Zou

Semi-supervised distribution learning

半监督分布学习

Abstract The causal inference literature frequently focuses on estimating the mean of the potential outcome, whereas quantiles of the potential outcome may carry important additional information. We propose the inverse estimating equations framework to generalize a wide class of causal inference solutions from estimating the mean of the potential outcome to its quantiles. We assume that a moment function is available to identify the mean of the threshold-transformed potential outcome, based on which a convenient construction of the estimating equation of the quantiles of potential outcome is proposed. In addition, we give a general construction of the efficient influence functions of the mean and quantiles of potential outcomes, and establish their connection. We motivate estimators for the quantile estimands with the efficient influence function, and develop their asymptotic properties when either parametric models or data-adaptive machine learners are used to estimate the nuisance functions. A broad implication of our results is that one can rework the existing result for mean causal estimands to facilitate causal inference on quantiles. Our general results are illustrated by several analytical and numerical examples.

短名
Journal Impact	2.51
国际分区	STATISTICS & PROBABILITY(Q1)
期刊索引	SCI Q1中科院 2 区

ISSN	0006-3444, 1464-3510
h-index	132
国内分区	数学(2区)数学数学与计算生物学(1区)数学生物学(2区)数学统计学与概率论(2区)

涉及主题	数学统计经济计算机科学计量经济学机器学习医学应用数学物理生物人工智能
出版信息	出版商: Oxford University Press，出版周期: Quarterly，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 1965，原创研究文献占比: 100.00%，自引率:4.20%， Gold OA占比: 14.67%
平均审稿周期	网友分享经验：较慢,6-12周
平均录用比例	网友分享经验：较易

Biometrika

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