发布求助
文献互助 智能选刊 最新文献

Biometrika最新文献

筛选
英文 中文
More Power by Using Fewer Permutations 用更少的排列组合获得更大的能量
IF 2.7 2区 数学
Biometrika Pub Date : 2024-07-10 DOI: 10.1093/biomet/asae031
Nick W Koning
{"title":"More Power by Using Fewer Permutations","authors":"Nick W Koning","doi":"10.1093/biomet/asae031","DOIUrl":"https://doi.org/10.1093/biomet/asae031","url":null,"abstract":"Summary It is conventionally believed that permutation-based testing methods should ideally use all permutations. We challenge this by showing we can sometimes obtain dramatically more power by using a tiny subgroup. As the subgroup is tiny, this also comes at a much lower computational cost. Moreover, the method remains valid for the same hypotheses. We exploit this to improve the popular permutation-based Westfall & Young MaxT multiple testing method. We analyze the relative efficiency in a Gaussian location model, and find the largest gain in high dimensions.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"377 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141585906","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
Testing Independence for Sparse Longitudinal Data 测试稀疏纵向数据的独立性
IF 2.7 2区 数学
Biometrika Pub Date : 2024-07-08 DOI: 10.1093/biomet/asae035
Changbo Zhu, Junwen Yao, Jane-Ling Wang
{"title":"Testing Independence for Sparse Longitudinal Data","authors":"Changbo Zhu, Junwen Yao, Jane-Ling Wang","doi":"10.1093/biomet/asae035","DOIUrl":"https://doi.org/10.1093/biomet/asae035","url":null,"abstract":"Summary With the advance of science and technology, more and more data are collected in the form of functions. A fundamental question for a pair of random functions is to test whether they are independent. This problem becomes quite challenging when the random trajectories are sampled irregularly and sparsely for each subject. In other words, each random function is only sampled at a few time-points, and these time-points vary with subjects. Furthermore, the observed data may contain noise. To the best of our knowledge, there exists no consistent test in the literature to test the independence of sparsely observed functional data. We show in this work that testing pointwise independence simultaneously is feasible. The test statistics are constructed by integrating pointwise distance covariances (Székely et al., 2007) and are shown to converge, at a certain rate, to their corresponding population counterparts, which characterize the simultaneous pointwise independence of two random functions. The performance of the proposed methods is further verified by Monte Carlo simulations and analysis of real data.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"18 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141566703","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
Semiparametric efficiency gains from parametric restrictions on propensity scores 倾向分数参数限制带来的半参数效率收益
IF 2.7 2区 数学
Biometrika Pub Date : 2024-07-06 DOI: 10.1093/biomet/asae034
Haruki Kono
{"title":"Semiparametric efficiency gains from parametric restrictions on propensity scores","authors":"Haruki Kono","doi":"10.1093/biomet/asae034","DOIUrl":"https://doi.org/10.1093/biomet/asae034","url":null,"abstract":"Summary We explore how much knowing a parametric restriction on propensity scores improves semiparametric efficiency bounds in the potential outcome framework. For stratified propensity scores, considered as a parametric model, we derive explicit formulas for the efficiency gain from knowing how the covariate space is split. Based on these, we find that the efficiency gain decreases as the partition of the stratification becomes finer. For general parametric models, where it is hard to obtain explicit representations of efficiency bounds, we propose a novel framework that enables us to see whether knowing a parametric model is valuable in terms of efficiency even when it is high-dimensional. In addition to the intuitive fact that knowing the parametric model does not help much if it is sufficiently flexible, we discover that the efficiency gain can be nearly zero even though the parametric assumption significantly restricts the space of possible propensity scores.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"22 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141566705","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
Debiasing Welch’s Method for Spectral Density Estimation 用于频谱密度估计的去偏差韦尔奇方法
IF 2.7 2区 数学
Biometrika Pub Date : 2024-07-01 DOI: 10.1093/biomet/asae033
Lachlan C Astfalck, Adam M Sykulski, Edward J Cripps
{"title":"Debiasing Welch’s Method for Spectral Density Estimation","authors":"Lachlan C Astfalck, Adam M Sykulski, Edward J Cripps","doi":"10.1093/biomet/asae033","DOIUrl":"https://doi.org/10.1093/biomet/asae033","url":null,"abstract":"Summary Welch’s method provides an estimator of the power spectral density that is statistically consistent. This is achieved by averaging over periodograms calculated from overlapping segments of a time series. For a finite length time series, while the variance of the estimator decreases as the number of segments increase, the magnitude of the estimator’s bias increases: a bias-variance trade-off ensues when setting the segment number. We address this issue by providing a novel method for debiasing Welch’s method which maintains the computational complexity and asymptotic consistency, and leads to improved finite-sample performance. Theoretical results are given for fourth-order stationary processes with finite fourth-order moments and absolutely convergent fourth-order cumulant function. The significant bias reduction is demonstrated with numerical simulation and an application to real-world data. Our estimator also permits irregular spacing over frequency and we demonstrate how this may be employed for signal compression and further variance reduction. Code accompanying this work is available in R and python.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"7 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141517612","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
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
{"title":"Testing serial dependence or cross dependence for time series with underreporting","authors":"Keyao Wei, Lengyang Wang, Yingcun Xia","doi":"10.1093/biomet/asae027","DOIUrl":"https://doi.org/10.1093/biomet/asae027","url":null,"abstract":"In practice, it is common for collected data to be underreported, which is particularly prevalent in fields such as social sciences, ecology and epidemiology. Drawing inferences from such data using conventional statistical methods can lead to incorrect conclusions. In this paper, we study tests for serial or cross dependence in time series data that are subject to underreporting. We introduce new test statistics, develop corresponding group-of-blocks bootstrap techniques, and establish their consistency. The methods are shown to be efficient by simulation and are used to identify key factors responsible for the spread of dengue fever and the occurrence of cardiovascular disease.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"197 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141509968","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
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
{"title":"A Rank-Based Sequential Test of Independence","authors":"Alexander Henzi, Michael Law","doi":"10.1093/biomet/asae023","DOIUrl":"https://doi.org/10.1093/biomet/asae023","url":null,"abstract":"Summary We consider the problem of independence testing for two univariate random variables in a sequential setting. By leveraging recent developments on safe, anytime-valid inference, we propose a test with time-uniform type I error control and derive explicit bounds on the finite sample performance of the test. We demonstrate the empirical performance of the procedure in comparison to existing sequential and non-sequential independence tests. Furthermore, since the proposed test is distribution free under the null hypothesis, we empirically simulate the gap due to Ville’s inequality–the supermartingale analogue of Markov’s inequality–that is commonly applied to control type I error in anytime-valid inference, and apply this to construct a truncated sequential test.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"23 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141060585","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
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
{"title":"Sharp symbolic nonparametric bounds for measures of benefit in observational and imperfect randomized studies with ordinal outcomes","authors":"Erin E Gabriel, Michael C Sachs, Andreas Kryger Jensen","doi":"10.1093/biomet/asae020","DOIUrl":"https://doi.org/10.1093/biomet/asae020","url":null,"abstract":"Summary The probability of benefit is a valuable and meaningful measure of treatment effect, which has advantages over the average treatment effect. Particularly for an ordinal outcome, it has a better interpretation and can make apparent different aspects of the treatment impact. Unfortunately, this measure, and variations of it, are not identifiable even in randomized trials with perfect compliance. There is, for this reason, a long literature on nonparametric bounds for unidentifiable measures of benefit. These have primarily focused on perfect randomized trial settings and one or two specific estimands. We expand these bounds to observational settings with unmeasured confounders and imperfect randomized trials for all three estimands considered in the literature: the probability of benefit, the probability of no harm, and the relative treatment effect.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"49 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140568397","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
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
{"title":"Individualized dynamic model for multi-resolutional data","authors":"J Zhang, F Xue, Q Xu, J Lee, A Qu","doi":"10.1093/biomet/asae015","DOIUrl":"https://doi.org/10.1093/biomet/asae015","url":null,"abstract":"SUMMARY Mobile health has emerged as a major success for tracking individual health status, due to the popularity and power of smartphones and wearable devices. This has also brought great challenges in handling heterogeneous, multi-resolution data which arise ubiquitously in mobile health due to irregular multivariate measurements collected from individuals. In this paper, we propose an individualized dynamic latent factor model for irregular multi-resolution time series data to interpolate unsampled measurements of time series with low resolution. One major advantage of the proposed method is the capability to integrate multiple irregular time series and multiple subjects by mapping the multi-resolution data to the latent space. In addition, the proposed individualized dynamic latent factor model is applicable to capturing heterogeneous longitudinal information through individualized dynamic latent factors. Our theory provides a bound on the integrated interpolation error and the convergence rate for B-spline approximation methods. Both the simulation studies and the application to smartwatch data demonstrate the superior performance of the proposed method compared to existing methods.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"3 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140568582","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信