Biometrika最新文献_第2页

Testing serial dependence or cross dependence for time series with underreporting 测试有漏报的时间序列的序列依赖性或交叉依赖性

IF 2.7 2区数学

Biometrika Pub Date : 2024-06-22 DOI: 10.1093/biomet/asae027

Keyao Wei, Lengyang Wang, Yingcun Xia

引用次数: 0

A Rank-Based Sequential Test of Independence 基于等级的独立性序列检验

IF 2.7 2区数学

Biometrika Pub Date : 2024-05-13 DOI: 10.1093/biomet/asae023

Alexander Henzi, Michael Law

引用次数: 0

A model-free variable screening method for optimal treatment regimes with high-dimensional survival data 利用高维生存数据优化治疗方案的无模型变量筛选法

IF 2.7 2区数学

Biometrika Pub Date : 2024-05-05 DOI: 10.1093/biomet/asae022

Cheng-Han Yang, Yu-Jen Cheng

{"title":"A model-free variable screening method for optimal treatment regimes with high-dimensional survival data","authors":"Cheng-Han Yang, Yu-Jen Cheng","doi":"10.1093/biomet/asae022","DOIUrl":"https://doi.org/10.1093/biomet/asae022","url":null,"abstract":"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 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 real dataset.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"46 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140883240","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

Sensitivity analysis for matched observational studies with continuous exposures and binary outcomes 对连续暴露和二元结果的匹配观察研究进行敏感性分析

IF 2.7 2区数学

Biometrika Pub Date : 2024-04-13 DOI: 10.1093/biomet/asae021

Jeffrey Zhang, Dylan S Small, Siyu Heng

{"title":"Sensitivity analysis for matched observational studies with continuous exposures and binary outcomes","authors":"Jeffrey Zhang, Dylan S Small, Siyu Heng","doi":"10.1093/biomet/asae021","DOIUrl":"https://doi.org/10.1093/biomet/asae021","url":null,"abstract":"Summary Matching is one of the most widely used study designs for adjusting for measured confounders in observational studies. However, unmeasured confounding may exist and cannot be removed by matching. Therefore, a sensitivity analysis is typically needed to assess a causal conclusion’s sensitivity to unmeasured confounding. Sensitivity analysis frameworks for binary exposures have been well-established for various matching designs and are commonly used in various studies. However, unlike the binary exposure case, there still lacks valid and general sensitivity analysis methods for continuous exposures, except in some special cases such as pair matching. To fill this gap in the binary outcome case, we develop a sensitivity analysis framework for general matching designs with continuous exposures and binary outcomes. First, we use probabilistic lattice theory to show our sensitivity analysis approach is finite-population- exact under Fisher’s sharp null. Second, we prove a novel design sensitivity formula as a powerful tool for asymptotically evaluating the performance of our sensitivity analysis approach. Third, to allow effect heterogeneity with binary outcomes, we introduce a framework for conducting asymptotically exact inference and sensitivity analysis on generalized attributable effects with binary outcomes via mixed- integer programming. Fourth, for the continuous outcomes case, we show that conducting an asymptotically exact sensitivity analysis in matched observational studies when both the exposures and outcomes are continuous is generally NP-hard, except in some special cases such as pair matching. As a real data application, we apply our new methods to study the effect of early-life lead exposure on juvenile delinquency. An implementation of the methods in this work is available in the R package doseSens.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"1 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140568514","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

Sharp symbolic nonparametric bounds for measures of benefit in observational and imperfect randomized studies with ordinal outcomes 具有序数结果的观察性研究和不完全随机研究中收益测量的锐利符号非参数界限

IF 2.7 2区数学

Biometrika Pub Date : 2024-04-11 DOI: 10.1093/biomet/asae020

Erin E Gabriel, Michael C Sachs, Andreas Kryger Jensen

引用次数: 0

Individualized dynamic model for multi-resolutional data 多分辨率数据的个性化动态模型

IF 2.7 2区数学

Biometrika Pub Date : 2024-04-08 DOI: 10.1093/biomet/asae015

J Zhang, F Xue, Q Xu, J Lee, A Qu

引用次数: 0

Flexible control of the median of the false discovery proportion 灵活控制错误发现比例的中位数

IF 2.7 2区数学

Biometrika Pub Date : 2024-03-23 DOI: 10.1093/biomet/asae018

Jesse Hemerik, Aldo Solari, Jelle J Goeman

引用次数: 0

Optimal regimes for algorithm-assisted human decision-making 算法辅助人类决策的最佳机制

IF 2.7 2区数学

Biometrika Pub Date : 2024-03-19 DOI: 10.1093/biomet/asae016

M J Stensrud, J D Laurendeau, A L Sarvet

引用次数: 0

Inference of partial correlations of a multivariate Gaussian time series 多变量高斯时间序列的局部相关性推理

IF 2.7 2区数学

Biometrika Pub Date : 2024-02-26 DOI: 10.1093/biomet/asae012

A S DiLernia, M Fiecas, L Zhang

引用次数: 0

Network-adjusted covariates for community detection 用于群落探测的网络调整协变量

IF 2.7 2区数学

Biometrika Pub Date : 2024-02-24 DOI: 10.1093/biomet/asae011

Y Hu, W Wang

{"title":"Network-adjusted covariates for community detection","authors":"Y Hu, W Wang","doi":"10.1093/biomet/asae011","DOIUrl":"https://doi.org/10.1093/biomet/asae011","url":null,"abstract":"Community detection is a crucial task in network analysis that can be significantly improved by incorporating subject-level information, i.e., covariates. Existing methods have shown the effectiveness of using covariates on the low-degree nodes, but rarely discuss the case where communities have significantly different density levels, i.e. multiscale networks. In this paper, we introduce a novel method that addresses this challenge by constructing network-adjusted covariates, which leverage the network connections and covariates with a node-specific weight for each node. This weight can be calculated without tuning parameters. We present novel theoretical results on the strong consistency of our method under degree-corrected stochastic blockmodels with covariates, even in the presence of misspecification and multiple sparse communities. Additionally, we establish a general lower bound for the community detection problem when both network and covariates are present, and it shows our method is optimal for connection intensity up to a constant factor. Our method outperforms existing approaches in simulations and a LastFM app user network. We then compare our method with others on a statistics publication citation network where 30% of nodes are isolated, and our method produces reasonable and balanced results.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"35 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139950512","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