Biometrika最新文献_第7页

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

An anomaly arising in the analysis of processes with more than one source of variability 在分析具有一个以上变率源的过程时出现的异常

IF 2.7 2区数学

Biometrika Pub Date : 2023-07-18 DOI: 10.1093/biomet/asad044

H. Battey, P. McCullagh

引用次数: 0

A cross-validation-based statistical theory for point processes 基于交叉验证的点过程统计理论

IF 2.7 2区数学

Biometrika Pub Date : 2023-06-27 DOI: 10.1093/biomet/asad041

O. Cronie, M. Moradi, C. Biscio

{"title":"A cross-validation-based statistical theory for point processes","authors":"O. Cronie, M. Moradi, C. Biscio","doi":"10.1093/biomet/asad041","DOIUrl":"https://doi.org/10.1093/biomet/asad041","url":null,"abstract":"\u0000 Motivated by cross-validation’s general ability to reduce overfitting and mean square error, we develop a cross-validation-based statistical theory for general point processes. It is based on the combination of two novel concepts for general point processes: cross-validation and prediction errors. Our cross-validation approach uses thinning to split a point process/pattern into pairs of training and validation sets, while our prediction errors measure discrepancy between two point processes. The new statistical approach, which may be used to model different distributional characteristics, exploits the prediction errors to measure how well a given model predicts validation sets using associated training sets. Having indicated that our new framework generalizes many existing statistical approaches, we then establish different theoretical properties for it, including large sample properties. We further recognize that non-parametric intensity estimation is an instance of Papangelou conditional intensity estimation, which we exploit to apply our new statistical theory to kernel intensity estimation. Using independent thinning-based cross-validation, we numerically show that the new approach substantially outperforms the state of the art in bandwidth selection. Finally, we carry out intensity estimation for a dataset in forestry (Euclidean domain) and a dataset in neurology (linear network).","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45141737","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