Bayesian Analysis最新文献_第9页

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

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":"1 1","pages":""},"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

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":" ","pages":""},"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

Biclustering via Semiparametric Bayesian Inference 基于半参数贝叶斯推理的双聚类

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-01-01 DOI: 10.1214/21-ba1284

Alejandro Murua, F. Quintana

引用次数: 2

Error Control of the Numerical Posterior with Bayes Factors in Bayesian Uncertainty Quantification 贝叶斯不确定性量化中贝叶斯因子的数值后验误差控制

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-01-01 DOI: 10.1214/20-ba1255

Marcos A. Capistrán, J. Christen, M. Daza-Torres, Hugo Flores-Arguedas, J. Montesinos-López

{"title":"Error Control of the Numerical Posterior with Bayes Factors in Bayesian Uncertainty Quantification","authors":"Marcos A. Capistrán, J. Christen, M. Daza-Torres, Hugo Flores-Arguedas, J. Montesinos-López","doi":"10.1214/20-ba1255","DOIUrl":"https://doi.org/10.1214/20-ba1255","url":null,"abstract":". In this paper, we address the numerical posterior error control problem for the Bayesian approach to inverse problems or recently known as Bayesian Uncertainty Quantiﬁcation (UQ). We generalize the results of Capistr´an et al. (2016) to (a priori) expected Bayes factors (BF) and in a more general, inﬁnite-dimensional setting. In this inverse problem, the unavoidable numerical approximation of the Forward Map (FM, i.e., the regressor function), arising from the numerical solution of a system of diﬀerential equations, demands error estimates of the corresponding approximate numerical posterior distribution. Our approach is to make such comparisons in the setting of Bayesian model selection and BFs. The main result of this paper is a bound on the absolute global error tolerated by the numerical solver of the FM in order to keep the BF of the numerical versus the theoretical posterior near one. For two examples, we provide a detailed analysis of the computation and implementation of the introduced bound. Furthermore, we show that the resulting numerical posterior turns out to be nearly identical from the theoretical posterior, given the control of the BF near one.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42140508","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: 2

Optimal Shrinkage Estimation of Predictive Densities Under α-Divergences α-散度下预测密度的最优收缩估计

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-01-01 DOI: 10.1214/21-BA1264

E. George, Gourab Mukherjee, Keisuke Yano

引用次数: 1

Perfect Sampling of the Posterior in the Hierarchical Pitman-Yor Process. 分层Pitman-Yor过程中后验的完美抽样。

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-01-01 DOI: 10.1214/21-BA1269

S. Bacallado, S. Favaro, Samuel Power, L. Trippa

引用次数: 1

On a Dirichlet Process Mixture Representation of Phase-Type Distributions 相型分布的Dirichlet过程混合表示

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2021-01-01 DOI: 10.1214/21-BA1272

Daniel Ayala, Leonardo Jofré, Luis Gutiérrez, R. H. Mena

引用次数: 0

Bayesian Nonstationary and Nonparametric Covariance Estimation for Large Spatial Data 大空间数据的贝叶斯非平稳和非参数协方差估计

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2020-12-10 DOI: 10.1214/21-ba1273

Brian Kidd, M. Katzfuss

引用次数: 8

A Dirichlet Process Mixture Model for Non-Ignorable Dropout 不可忽略衰减的Dirichlet过程混合模型

IF 4.4 2区数学

Bayesian Analysis Pub Date : 2020-12-01 DOI: 10.1214/19-ba1181

Camille M. Moore, N. Carlson, S. MaWhinney, S. Kreidler

{"title":"A Dirichlet Process Mixture Model for Non-Ignorable Dropout","authors":"Camille M. Moore, N. Carlson, S. MaWhinney, S. Kreidler","doi":"10.1214/19-ba1181","DOIUrl":"https://doi.org/10.1214/19-ba1181","url":null,"abstract":". Longitudinal cohorts are a valuable resource for studying HIV disease progression; however, dropout is common in these studies. Subjects often fail to re-turn for visits due to disease progression, loss to follow-up, or death. When dropout depends on unobserved outcomes, data are missing not at random, and results from standard longitudinal data analyses can be biased. Several methods have been proposed to adjust for non-ignorable dropout; however, many of these approaches rely on parametric assumptions about the distribution of dropout times and the functional form of the relationship between the outcome and dropout time. More ﬂexible approaches may be needed when the distribution of dropout times does not follow a known distribution or violates proportional hazards assumptions, or when the relationship between the outcome and dropout times does not have a simple polynomial form. We propose a Bayesian semi-parametric Dirichlet process mixture model to ﬂexibly model the relationship between dropout time and the outcome and show that more accurate inference can be obtained by non-parametrically modeling the distribution of subject-speciﬁc eﬀects as well as the distribution of dropout times. Results from simulation studies as well as an application to a longitudinal HIV cohort study database illustrate the strengths of our Bayesian semi-parametric approach.","PeriodicalId":55398,"journal":{"name":"Bayesian Analysis","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49479134","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