Biometrika最新文献_第7页

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

Estimation of prediction error in time series 时间序列预测误差的估计

2区数学

Biometrika Pub Date : 2023-09-09 DOI: 10.1093/biomet/asad053

Alexander Aue, Prabir Burman

{"title":"Estimation of prediction error in time series","authors":"Alexander Aue, Prabir Burman","doi":"10.1093/biomet/asad053","DOIUrl":"https://doi.org/10.1093/biomet/asad053","url":null,"abstract":"Summary The accurate estimation of prediction errors in time series is an important problem, which has immediate implications for the accuracy of prediction intervals as well as the quality of a number of widely used time series model selection criteria such as the Akaike information criterion. Except for simple cases, however, it is difficult or even impossible to obtain exact analytical expressions for one-step and multi-step predictions. This may be one of the reasons that, unlike in the independent case (see Efron, 2004), up to now there has been no fully established methodology for time series prediction error estimation. Starting from an approximation to the bias-variance decomposition of the squared prediction error, a method for accurate estimation of prediction errors in both univariate and multivariate stationary time series is developed in this article. In particular, several estimates are derived for a general class of predictors that includes most of the popular linear, nonlinear, parametric and nonparametric time series models used in practice, with causal invertible autoregressive moving average and nonparametric autoregressive processes discussed as lead examples. Simulations demonstrate that the proposed estimators perform quite well in finite samples. The estimates may also be used for model selection when the purpose of modelling is prediction.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136108242","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

More Efficient Exact Group Invariance Testing: using a Representative Subgroup 更有效的精确群不变性测试：使用代表子群

IF 2.7 2区数学

Biometrika Pub Date : 2023-09-01 DOI: 10.1093/biomet/asad050

N. W. Koning, J. Hemerik

引用次数: 0

A generalized Bayes framework for probabilistic clustering. 概率聚类的广义Bayes框架

IF 2.4 2区数学

Biometrika Pub Date : 2023-09-01 Epub Date: 2023-01-19 DOI: 10.1093/biomet/asad004

Tommaso Rigon, Amy H Herring, David B Dunson

{"title":"A generalized Bayes framework for probabilistic clustering.","authors":"Tommaso Rigon, Amy H Herring, David B Dunson","doi":"10.1093/biomet/asad004","DOIUrl":"10.1093/biomet/asad004","url":null,"abstract":"Loss-based clustering methods, such as k-means clustering and its variants, are standard tools for finding groups in data. However, the lack of quantification of uncertainty in the estimated clusters is a disadvantage. Model-based clustering based on mixture models provides an alternative approach, but such methods face computational problems and are highly sensitive to the choice of kernel. In this article we propose a generalized Bayes framework that bridges between these paradigms through the use of Gibbs posteriors. In conducting Bayesian updating, the loglikelihood is replaced by a loss function for clustering, leading to a rich family of clustering methods. The Gibbs posterior represents a coherent updating of Bayesian beliefs without needing to specify a likelihood for the data, and can be used for characterizing uncertainty in clustering. We consider losses based on Bregman divergence and pairwise similarities, and develop efficient deterministic algorithms for point estimation along with sampling algorithms for uncertainty quantification. Several existing clustering algorithms, including k-means, can be interpreted as generalized Bayes estimators in our framework, and thus we provide a method of uncertainty quantification for these approaches, allowing, for example, calculation of the probability that a data point is well clustered.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":" ","pages":"559-578"},"PeriodicalIF":2.4,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11840691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46381325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Marginal proportional hazards models for multivariate interval-censored data. 多变量区间删失数据的边际比例危害模型。

IF 2.4 2区数学

Biometrika Pub Date : 2023-09-01 Epub Date: 2022-11-02 DOI: 10.1093/biomet/asac059

Yangjianchen Xu, Donglin Zeng, D Y Lin

{"title":"Marginal proportional hazards models for multivariate interval-censored data.","authors":"Yangjianchen Xu, Donglin Zeng, D Y Lin","doi":"10.1093/biomet/asac059","DOIUrl":"10.1093/biomet/asac059","url":null,"abstract":"Multivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption that all event times are independent, and we provide a simple and stable EM-type algorithm. The resulting nonparametric maximum pseudolikelihood estimators for the regression parameters are shown to be consistent and asymptotically normal, with a limiting covariance matrix that can be consistently estimated by a sandwich estimator under arbitrary dependence structures for the related event times. We evaluate the performance of the proposed methods through extensive simulation studies and present an application to data from the Atherosclerosis Risk in Communities Study.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"110 3","pages":"815-830"},"PeriodicalIF":2.4,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10434824/pdf/nihms-1874830.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10490393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ASSESSING TIME-VARYING CAUSAL EFFECT MODERATION IN THE PRESENCE OF CLUSTER-LEVEL TREATMENT EFFECT HETEROGENEITY AND INTERFERENCE. 在集群水平治疗效果异质性和干扰存在的情况下评估时变因果效应调节。

IF 2.7 2区数学

Biometrika Pub Date : 2023-09-01 DOI: 10.1093/biomet/asac065

Jieru Shi, Zhenke Wu, Walter Dempsey

引用次数: 2

Deep Kronecker Network 深度克罗内克网络

2区数学

Biometrika Pub Date : 2023-08-31 DOI: 10.1093/biomet/asad049

Long Feng, Guang Yang

{"title":"Deep Kronecker Network","authors":"Long Feng, Guang Yang","doi":"10.1093/biomet/asad049","DOIUrl":"https://doi.org/10.1093/biomet/asad049","url":null,"abstract":"Summary We develop a novel framework named Deep Kronecker Network for the analysis of medical imaging data, including magnetic resonance imaging (MRI), functional MRI, computed tomography, and more. Medical imaging data differs from general images in two main aspects: i) the sample size is often considerably smaller, and ii) the interpretation of the model is usually more crucial than predicting the outcome. As a result, standard methods such as convolutional neural networks cannot be directly applied to medical imaging analysis. Therefore, we propose the Deep Kronecker Network, which can adapt to the low sample size constraint and offer the desired model interpretation. Our approach is versatile, as it works for both matrix and tensor represented image data and can be applied to discrete and continuous outcomes. The Deep Kronecker network is built upon a Kronecker product structure, which implicitly enforces a piecewise smooth property on coefficients. Moreover, our approach resembles a fully convolutional network as the Kronecker structure can be expressed in a convolutional form. Interestingly, our approach also has strong connections to the tensor regression framework proposed by Zhou et al. (2013), which imposes a canonical low-rank structure on tensor coefficients. We conduct both classification and regression analyses using real MRI data from the Alzheimer’s Disease Neuroimaging Initiative to demonstrate the effectiveness of our approach.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135830829","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

Kernel interpolation generalizes poorly 核插值的泛化性很差

2区数学

Biometrika Pub Date : 2023-08-07 DOI: 10.1093/biomet/asad048

Yicheng Li, Haobo Zhang, Qian Lin

引用次数: 5

τ -censored weighted Benjamini-Hochberg procedures under independence 独立性下τ -审查加权benjami - hochberg程序

IF 2.7 2区数学

Biometrika Pub Date : 2023-08-02 DOI: 10.1093/biomet/asad047

Haibing Zhao, Huijuan Zhou

{"title":"τ -censored weighted Benjamini-Hochberg procedures under independence","authors":"Haibing Zhao, Huijuan Zhou","doi":"10.1093/biomet/asad047","DOIUrl":"https://doi.org/10.1093/biomet/asad047","url":null,"abstract":"\u0000 In the field of multiple hypothesis testing, auxiliary information can be leveraged to enhance the efficiency of test procedures. A common way to make use of auxiliary information is by weighting p-values. However, when the weights are learned from data, controlling the finite-sample false discovery rate becomes challenging, and most existing weighted procedures only guarantee false discovery rate control in an asymptotic limit. In a recent study conducted by Ignatiadis & Huber (2021), a novel τ-censored weighted Benjamini-Hochberg procedure was proposed to control the finite-sample false discovery rate. The authors employed the cross-weighting approach to learn weights for the p-values. This approach randomly splits the data into several folds and constructs a weight for each p-value Pi using the p-values outside the fold containing Pi. Cross-weighting does not exploit the p-value information inside the fold and only balances the weights within each fold, which may result in a loss of power. In this article, we introduce two methods for constructing data-driven weights for τ-censored weighted Benjamini-Hochberg procedures under independence. They provide new insight into masking p-values to prevent overfitting in multiple testing. The first method utilizes a leave-one-out technique, where all but one of the p-values are used to learn a weight for each p-value. This technique masks the information of a p-value in its weight by calculating the infimum of the weight with respect to the p-value. The second method uses partial information from each p-value to construct weights and utilizes the conditional distributions of the null p-values to establish false discovery rate control. Additionally, we propose two methods for estimating the null proportion and demonstrate how to integrate null-proportion adaptivity into the proposed weights to improve power.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49253424","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

Online Inference with Debiased Stochastic Gradient Descent 基于去偏随机梯度下降的在线推理

IF 2.7 2区数学

Biometrika Pub Date : 2023-07-27 DOI: 10.1093/biomet/asad046

Ruijian Han, Lan Luo, Yuanyuan Lin, Jian Huang

引用次数: 3