Bayesian Analysis最新文献_第9页

On the Existence of Uniformly Most Powerful Bayesian Tests With Application to Non-Central Chi-Squared Tests. 一致最强贝叶斯检验的存在性及其在非中心卡方检验中的应用

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-03-01 Epub Date: 2020-01-07 DOI: 10.1214/19-ba1194

Amir Nikooienejad, Valen E Johnson

{"title":"On the Existence of Uniformly Most Powerful Bayesian Tests With Application to Non-Central Chi-Squared Tests.","authors":"Amir Nikooienejad, Valen E Johnson","doi":"10.1214/19-ba1194","DOIUrl":"https://doi.org/10.1214/19-ba1194","url":null,"abstract":"<p><p>Uniformly most powerful Bayesian tests (UMPBT's) are an objective class of Bayesian hypothesis tests that can be considered the Bayesian counterpart of classical uniformly most powerful tests. Because the rejection regions of UMPBT's can be matched to the rejection regions of classical uniformly most powerful tests (UMPTs), UMPBT's provide a mechanism for calibrating Bayesian evidence thresholds, Bayes factors, classical significance levels and p-values. The purpose of this article is to expand the application of UMPBT's outside the class of exponential family models. Specifically, we introduce sufficient conditions for the existence of UMPBT's and propose a unified approach for their derivation. An important application of our methodology is the extension of UMPBT's to testing whether the non-centrality parameter of a chi-squared distribution is zero. The resulting tests have broad applicability, providing default alternative hypotheses to compute Bayes factors in, for example, Pearson's chi-squared test for goodness-of-fit, tests of independence in contingency tables, and likelihood ratio, score and Wald tests.</p>","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8189570/pdf/nihms-1595140.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39014996","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: 2

Group Inverse-Gamma Gamma Shrinkage for Sparse Linear Models with Block-Correlated Regressors 具有块相关回归的稀疏线性模型的群逆伽玛-伽玛收缩

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-02-21 DOI: 10.1214/23-BA1371

Jonathan Boss, J. Datta, Xin Wang, S. Park, Jian Kang, B. Mukherjee

{"title":"Group Inverse-Gamma Gamma Shrinkage for Sparse Linear Models with Block-Correlated Regressors","authors":"Jonathan Boss, J. Datta, Xin Wang, S. Park, Jian Kang, B. Mukherjee","doi":"10.1214/23-BA1371","DOIUrl":"https://doi.org/10.1214/23-BA1371","url":null,"abstract":"Heavy-tailed continuous shrinkage priors, such as the horseshoe prior, are widely used for sparse estimation problems. However, there is limited work extending these priors to predictors with grouping structures. Of particular interest in this article, is regression coefficient estimation where pockets of high collinearity in the covariate space are contained within known covariate groupings. To assuage variance inflation due to multicollinearity we propose the group inverse-gamma gamma (GIGG) prior, a heavy-tailed prior that can trade-off between local and group shrinkage in a data adaptive fashion. A special case of the GIGG prior is the group horseshoe prior, whose shrinkage profile is correlated within-group such that the regression coefficients marginally have exact horseshoe regularization. We show posterior consistency for regression coefficients in linear regression models and posterior concentration results for mean parameters in sparse normal means models. The full conditional distributions corresponding to GIGG regression can be derived in closed form, leading to straightforward posterior computation. We show that GIGG regression results in low mean-squared error across a wide range of correlation structures and within-group signal densities via simulation. We apply GIGG regression to data from the National Health and Nutrition Examination Survey for associating environmental exposures with liver functionality.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2021-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47707520","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

Changepoint Detection on a Graph of Time Series 时间序列图上的变点检测

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-02-08 DOI: 10.1214/23-BA1365

K. L. Hallgren, N. Heard, Melissa J. M. Turcotte

引用次数: 0

Bayesian Uncertainty Quantification for Low-Rank Matrix Completion 低秩矩阵补全的贝叶斯不确定性量化

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-01-05 DOI: 10.1214/22-ba1317

H. Yuchi, Simon Mak, Yao Xie

{"title":"Bayesian Uncertainty Quantification for Low-Rank Matrix Completion","authors":"H. Yuchi, Simon Mak, Yao Xie","doi":"10.1214/22-ba1317","DOIUrl":"https://doi.org/10.1214/22-ba1317","url":null,"abstract":". We consider the problem of uncertainty quantiﬁcation for an unknown low-rank matrix X , given a partial and noisy observation of its entries. This quantiﬁcation of uncertainty is essential for many real-world problems, including image processing, satellite imaging, and seismology, providing a principled framework for validating scientiﬁc conclusions and guiding decision-making. However, existing literature has mainly focused on the completion (i.e., point estimation) of the matrix X , with little work on investigating its uncertainty. To this end, we propose in this work a new Bayesian modeling framework, called BayeSMG, which parametrizes the unknown X via its underlying row and column subspaces. This Bayesian subspace parametrization enables eﬃcient posterior inference on matrix subspaces, which represents interpretable phenomena in many applications. This can then be leveraged for improved matrix recovery. We demonstrate the eﬀective-ness of BayeSMG over existing Bayesian matrix recovery methods in numerical experiments, image inpainting, and a seismic sensor network application. This shows the proposed method can indeed provide better uncertainty quantiﬁcation of X via a fully-Bayesian model speciﬁcation on matrix subspaces.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2021-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46060925","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: 5

Bayesian Concentration Ratio and Dissonance 贝叶斯集中比与不协调

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-01-01 DOI: 10.1214/21-ba1277

Wei Shi, Ming-Hui Chen, L. Kuo, P. Lewis

引用次数: 0

Posterior Consistency of Factor Dimensionality in High-Dimensional Sparse Factor Models 高维稀疏因子模型中因子维数的后验一致性

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-01-01 DOI: 10.1214/21-BA1261

Ilsang Ohn, Yongdai Kim

引用次数: 8

Bayesian Survival Tree Ensembles with Submodel Shrinkage 具有子模型收缩的贝叶斯生存树集成

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-01-01 DOI: 10.1214/21-ba1285

A. Linero, Piyali Basak, Yinpu Li, D. Sinha

引用次数: 14

Bayesian Quickest Detection of Credit Card Fraud 信用卡欺诈的贝叶斯快速检测

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-01-01 DOI: 10.1214/20-ba1254

B. Buonaguidi, A. Mira, Herbert Bucheli, V. Vitanis

引用次数: 5

Bayesian Tensor Response Regression with an Application to Brain Activation Studies 贝叶斯张量响应回归及其在脑激活研究中的应用

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-01-01 DOI: 10.1214/21-ba1280

Rajarshi Guhaniyogi, Daniel Spencer

{"title":"Bayesian Tensor Response Regression with an Application to Brain Activation Studies","authors":"Rajarshi Guhaniyogi, Daniel Spencer","doi":"10.1214/21-ba1280","DOIUrl":"https://doi.org/10.1214/21-ba1280","url":null,"abstract":". This article proposes a novel Bayesian implementation of regression with multi-dimensional array (tensor) response on scalar covariates. The recent emergence of complex datasets in various disciplines presents a pressing need to devise regression models with a tensor valued response. This article considers one such application of detecting neuronal activation in fMRI experiments in presence of tensor valued brain images and scalar predictors. The overarching goal in this application is to identify spatial regions (voxels) of a brain activated by an external stimulus. In such and related applications, we propose to regress responses from all cells (or voxels in brain activation studies) together as a tensor response on scalar predictors, accounting for the structural information inherent in the tensor response. To estimate model parameters with proper cell speciﬁc shrinkage, we propose a novel multiway stick breaking shrinkage prior distribution on tensor structured regression coeﬃcients, enabling identiﬁcation of cells which are related to the predictors. The major novelty of this article lies in the theoretical study of the contraction properties for the proposed shrinkage prior in the tensor response regression when the number of cells grows faster than the sample size. Speciﬁcally, estimates of tensor regression coeﬃcients are shown to be asymptotically concen-trated around the true sparse tensor in L 2 -sense under mild assumptions. Various simulation studies and analysis of a brain activation data empirically verify desirable performance of the proposed model in terms of estimation and inference on cell-level parameters.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43385168","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: 19

Personalized Dynamic Treatment Regimes in Continuous Time: A Bayesian Approach for Optimizing Clinical Decisions with Timing 连续时间的个性化动态治疗方案:优化临床决策的贝叶斯方法

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-01-01 DOI: 10.1214/21-ba1276

William Hua, Hongyuan Mei, S. Zohar, M. Giral, Yanxun Xu

{"title":"Personalized Dynamic Treatment Regimes in Continuous Time: A Bayesian Approach for Optimizing Clinical Decisions with Timing","authors":"William Hua, Hongyuan Mei, S. Zohar, M. Giral, Yanxun Xu","doi":"10.1214/21-ba1276","DOIUrl":"https://doi.org/10.1214/21-ba1276","url":null,"abstract":"Accurate models of clinical actions and their impacts on disease progression are critical for estimating personalized optimal dynamic treatment regimes (DTRs) in medical/health research, especially in managing chronic conditions. Traditional statistical methods for DTRs usually focus on estimating the optimal treatment or dosage at each given medical intervention, but overlook the important question of “when this intervention should happen.” We fill this gap by developing a two-step Bayesian approach to optimize clinical decisions with timing. In the first step, we build a generative model for a sequence of medical interventions—which are discrete events in continuous time—with a marked temporal point process (MTPP) where the mark is the assigned treatment or dosage. Then this clinical action model is embedded into a Bayesian joint framework where the other components model clinical observations including longitudinal medical measurements and time-to-event data conditional on treatment histories. In the second step, we propose a policy gradient method to learn the personalized optimal clinical decision that maximizes the patient survival by interacting the MTPP with the model on clinical observations while accounting for uncertainties in clinical observations learned from the posterior inference of the Bayesian joint model in the first step. A signature application of the proposed approach is to schedule follow-up visitations and assign a dosage at each visitation for patients after kidney transplantation. We evaluate our approach with comparison to alternative methods on both simulated and real-world datasets. In our experiments, the personalized decisions made by the proposed method are clinically useful: they are interpretable and successfully help improve patient survival.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66086047","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: 11