Bayesian Analysis最新文献_第8页

Bayesian Optimal Experimental Design for Inferring Causal Structure 推理因果结构的贝叶斯最优实验设计

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-03-28 DOI: 10.1214/22-ba1335

M. Zemplenyi, Jeffrey W. Miller

引用次数: 3

Centered Partition Processes: Informative Priors for Clustering (with Discussion). 中心分区过程:聚类的信息先验(与讨论)。

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-03-01 DOI: 10.1214/20-BA1197

Sally Paganin, Amy H Herring, Andrew F Olshan, David B Dunson

{"title":"Centered Partition Processes: Informative Priors for Clustering (with Discussion).","authors":"Sally Paganin, Amy H Herring, Andrew F Olshan, David B Dunson","doi":"10.1214/20-BA1197","DOIUrl":"https://doi.org/10.1214/20-BA1197","url":null,"abstract":"There is a very rich literature proposing Bayesian approaches for clustering starting with a prior probability distribution on partitions. Most approaches assume exchangeability, leading to simple representations in terms of Exchangeable Partition Probability Functions (EPPF). Gibbs-type priors encompass a broad class of such cases, including Dirichlet and Pitman-Yor processes. Even though there have been some proposals to relax the exchangeability assumption, allowing covariate-dependence and partial exchangeability, limited consideration has been given on how to include concrete prior knowledge on the partition. For example, we are motivated by an epidemiological application, in which we wish to cluster birth defects into groups and we have prior knowledge of an initial clustering provided by experts. As a general approach for including such prior knowledge, we propose a Centered Partition (CP) process that modifies the EPPF to favor partitions close to an initial one. Some properties of the CP prior are described, a general algorithm for posterior computation is developed, and we illustrate the methodology through simulation examples and an application to the motivating epidemiology study of birth defects.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":"16 1","pages":"301-370"},"PeriodicalIF":4.4,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9364237/pdf/nihms-1815470.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10689706","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: 13

A Phase I-II Basket Trial Design to Optimize Dose-Schedule Regimes Based on Delayed Outcomes. 基于延迟结果优化剂量方案的I-II期篮子试验设计

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-03-01 Epub Date: 2020-03-28 DOI: 10.1214/20-ba1205

Ruitao Lin, Peter F Thall, Ying Yuan

{"title":"A Phase I-II Basket Trial Design to Optimize Dose-Schedule Regimes Based on Delayed Outcomes.","authors":"Ruitao Lin, Peter F Thall, Ying Yuan","doi":"10.1214/20-ba1205","DOIUrl":"https://doi.org/10.1214/20-ba1205","url":null,"abstract":"This paper proposes a Bayesian adaptive basket trial design to optimize the dose-schedule regimes of an experimental agent within disease subtypes, called \"baskets\", for phase I-II clinical trials based on late-onset efficacy and toxicity. To characterize the association among the baskets and regimes, a Bayesian hierarchical model is assumed that includes a heterogeneity parameter, adaptively updated during the trial, that quantifies information shared across baskets. To account for late-onset outcomes when doing sequential decision making, unobserved outcomes are treated as missing values and imputed by exploiting early biomarker and low-grade toxicity information. Elicited joint utilities of efficacy and toxicity are used for decision making. Patients are randomized adaptively to regimes while accounting for baskets, with randomization probabilities proportional to the posterior probability of achieving maximum utility. Simulations are presented to assess the design's robustness and ability to identify optimal dose-schedule regimes within disease subtypes, and to compare it to a simplified design that treats the subtypes independently.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":" ","pages":"179-202"},"PeriodicalIF":4.4,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277108/pdf/nihms-1714036.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39191577","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: 8

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":"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.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":" ","pages":"93-109"},"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":" ","pages":""},"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":" ","pages":""},"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

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 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

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