39 results match your criteria Bayesian Analysis[Journal]

  • Page 1 of 1

Joining and splitting models with Markov melding.

Bayesian Anal 2019 Jan;14(1):81-109

MRC Biostatistics Unit, University of Cambridge, United Kingdom.

Analysing multiple evidence sources is often feasible only via a modular approach, with separate submodels specified for smaller components of the available evidence. Here we introduce a generic framework that enables fully Bayesian analysis in this setting. We propose a generic method for forming a suitable joint model when submodels, and a convenient computational algorithm for fitting this joint model in stages, rather than as a single, monolithic model. Read More

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http://dx.doi.org/10.1214/18-BA1104DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6324725PMC
January 2019

A New Monte Carlo Method for Estimating Marginal Likelihoods.

Bayesian Anal 2018 Jun 28;13(2):311-333. Epub 2017 Feb 28.

Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT 06269, USA.

Evaluating the marginal likelihood in Bayesian analysis is essential for model selection. Estimators based on a single Markov chain Monte Carlo sample from the posterior distribution include the harmonic mean estimator and the inflated density ratio estimator. We propose a new class of Monte Carlo estimators based on this single Markov chain Monte Carlo sample. Read More

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http://dx.doi.org/10.1214/17-BA1049DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5967857PMC
June 2018
1 Read

Locally Adaptive Smoothing with Markov Random Fields and Shrinkage Priors.

Bayesian Anal 2018 Mar 24;13(1):225-252. Epub 2017 Feb 24.

Quantitative Ecology and Resource Management, University of Washington, Seattle, WA 98195.

We present a locally adaptive nonparametric curve fitting method that operates within a fully Bayesian framework. This method uses shrinkage priors to induce sparsity in order- differences in the latent trend function, providing a combination of local adaptation and global control. Using a scale mixture of normals representation of shrinkage priors, we make explicit connections between our method and th order Gaussian Markov random field smoothing. Read More

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http://dx.doi.org/10.1214/17-BA1050DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5942601PMC
March 2018
1 Read

Towards a Multidimensional Approach to Bayesian Disease Mapping.

Bayesian Anal 2017 Mar 18;12(1):239-259. Epub 2016 Mar 18.

University of California, Los Angeles (UCLA), USA.

Multivariate disease mapping enriches traditional disease mapping studies by analysing several diseases jointly. This yields improved estimates of the geographical distribution of risk from the diseases by enabling borrowing of information across diseases. Beyond multivariate smoothing for several diseases, several other variables, such as sex, age group, race, time period, and so on, could also be jointly considered to derive multivariate estimates. Read More

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http://dx.doi.org/10.1214/16-BA995DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5918309PMC

High-Dimensional Bayesian Geostatistics.

Authors:
Sudipto Banerjee

Bayesian Anal 2017 Jun 16;12(2):583-614. Epub 2017 May 16.

UCLA Department of Biostatistics, 650 Charles E. Young Drive South, Los Angeles, CA 90095-1772.

With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points. Over the last decade, hierarchical spatiotemporal process models have become widely deployed statistical tools for researchers to better understand the complex nature of spatial and temporal variability. However, fitting hierarchical spatiotemporal models often involves expensive matrix computations with complexity increasing in cubic order for the number of spatial locations and temporal points. Read More

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http://dx.doi.org/10.1214/17-BA1056RDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5790125PMC
June 2017
3 Reads

A Decision-Theoretic Comparison of Treatments to Resolve Air Leaks After Lung Surgery Based on Nonparametric Modeling.

Bayesian Anal 2017 26;12(3):639-652. Epub 2016 Jul 26.

Dept. of Thoracic Surgery, The University of Texas M.D. Anderson Cancer Center.

We propose a Bayesian nonparametric utility-based group sequential design for a randomized clinical trial to compare a gel sealant to standard care for resolving air leaks after pulmonary resection. Clinically, resolving air leaks in the days soon after surgery is highly important, since longer resolution time produces undesirable complications that require extended hospitalization. The problem of comparing treatments is complicated by the fact that the resolution time distributions are skewed and multi-modal, so using means is misleading. Read More

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http://dx.doi.org/10.1214/16-BA1016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5613677PMC
July 2016
7 Reads

Posterior Contraction Rates of the Phylogenetic Indian Buffet Processes.

Bayesian Anal 2016 Jun 5;11(2):477-497. Epub 2015 Jun 5.

University of North Carolina, Chapel Hill.

By expressing prior distributions as general stochastic processes, nonparametric Bayesian methods provide a flexible way to incorporate prior knowledge and constrain the latent structure in statistical inference. The Indian buffet process (IBP) is such an example that can be used to define a prior distribution on infinite binary features, where the exchangeability among subjects is assumed. The phylogenetic Indian buffet process (pIBP), a derivative of IBP, enables the modeling of non-exchangeability among subjects through a stochastic process on a rooted tree, which is similar to that used in phylogenetics, to describe relationships among the subjects. Read More

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http://dx.doi.org/10.1214/15-BA958DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4830498PMC
June 2016
12 Reads

Objective Bayesian Inference for Bilateral Data.

Bayesian Anal 2015 Mar 28;10(1):139-170. Epub 2015 Jan 28.

Department of Statistics, University of Connecticut, Storrs, CT 06269, USA.

This paper presents three objective Bayesian methods for analyzing bilateral data under Dallal's model and the saturated model. Three parameters are of interest, namely, the risk difference, the risk ratio, and the odds ratio. We derive Jeffreys' prior and Bernardo's reference prior associated with the three parameters that characterize Dallal's model. Read More

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http://projecteuclid.org/euclid.ba/1422468426
Publisher Site
http://dx.doi.org/10.1214/14-BA890DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4821170PMC
March 2015
1 Read

Pre-Surgical fMRI Data Analysis Using a Spatially Adaptive Conditionally Autoregressive Model.

Bayesian Anal 2016 Jun 26;11(2):599-625. Epub 2015 Aug 26.

Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109;

Spatial smoothing is an essential step in the analysis of functional magnetic resonance imaging (fMRI) data. One standard smoothing method is to convolve the image data with a three-dimensional Gaussian kernel that applies a fixed amount of smoothing to the entire image. In pre-surgical brain image analysis where spatial accuracy is paramount, this method, however, is not reasonable as it can blur the boundaries between activated and deactivated regions of the brain. Read More

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http://dx.doi.org/10.1214/15-BA972DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4814103PMC
June 2016
2 Reads

Flexible Bayesian survival modeling with semiparametric time-dependent and shape-restricted covariate effects.

Bayesian Anal 2016 Jun 14;11(2):381-402. Epub 2015 May 14.

Division of Biostatistics, The University of Minnesota.

Presently, there are few options with available software to perform a fully Bayesian analysis of time-to-event data wherein the hazard is estimated semi- or non-parametrically. One option is the piecewise exponential model, which requires an often unrealistic assumption that the hazard is piecewise constant over time. The primary aim of this paper is to construct a tractable semiparametric alternative to the piecewise exponential model that assumes the hazard is continuous, and to provide modifiable, user-friendly software that allows the use of these methods in a variety of settings. Read More

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http://dx.doi.org/10.1214/15-BA954DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4811615PMC
June 2016
8 Reads

Restricted Covariance Priors with Applications in Spatial Statistics.

Bayesian Anal 2015 Dec 4;10(4):965-990. Epub 2015 Feb 4.

Departments of Statistics, Biobehavioral Nursing, and Health Systems and the Center for Statistics and the Social Sciences, University of Washington, Seattle, WA 98195.

We present a Bayesian model for area-level count data that uses Gaussian random effects with a novel type of G-Wishart prior on the inverse variance- covariance matrix. Specifically, we introduce a new distribution called the truncated G-Wishart distribution that has support over precision matrices that lead to positive associations between the random effects of neighboring regions while preserving conditional independence of non-neighboring regions. We describe Markov chain Monte Carlo sampling algorithms for the truncated G-Wishart prior in a disease mapping context and compare our results to Bayesian hierarchical models based on intrinsic autoregression priors. Read More

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http://dx.doi.org/10.1214/14-BA927DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4705859PMC
December 2015
2 Reads

Bayesian Polynomial Regression Models to Fit Multiple Genetic Models for Quantitative Traits.

Bayesian Anal 2015 Mar;10(1):53-74

Department of Biostatistics, Boston University School of Public Health.

We present a coherent Bayesian framework for selection of the most likely model from the five genetic models (genotypic, additive, dominant, co-dominant, and recessive) commonly used in genetic association studies. The approach uses a polynomial parameterization of genetic data to simultaneously fit the five models and save computations. We provide a closed-form expression of the marginal likelihood for normally distributed data, and evaluate the performance of the proposed method and existing method through simulated and real genome-wide data sets. Read More

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http://dx.doi.org/10.1214/14-BA880DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4446790PMC
March 2015
2 Reads

Spatial Bayesian Variable Selection Models on Functional Magnetic Resonance Imaging Time-Series Data.

Bayesian Anal 2014 ;9(3):699-732

Department of Psychiatry, Johns Hopkins University.

A common objective of fMRI (functional magnetic resonance imaging) studies is to determine subject-specific areas of increased blood oxygenation level dependent (BOLD) signal contrast in response to a stimulus or task, and hence to infer regional neuronal activity. We posit and investigate a Bayesian approach that incorporates spatial and temporal dependence and allows for the task-related change in the BOLD signal to change dynamically over the scanning session. In this way, our model accounts for potential learning effects in addition to other mechanisms of temporal drift in task-related signals. Read More

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http://dx.doi.org/10.1214/14-BA873DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4268890PMC
January 2014

On Numerical Aspects of Bayesian Model Selection in High and Ultrahigh-dimensional Settings.

Authors:
Valen E Johnson

Bayesian Anal 2013 Dec;8(4):741-758

Department of Statistics, Texas A&M University.

This article examines the convergence properties of a Bayesian model selection procedure based on a non-local prior density in ultrahigh-dimensional settings. The performance of the model selection procedure is also compared to popular penalized likelihood methods. Coupling diagnostics are used to bound the total variation distance between iterates in an Markov chain Monte Carlo (MCMC) algorithm and the posterior distribution on the model space. Read More

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http://dx.doi.org/10.1214/13-BA818DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3968919PMC
December 2013
1 Read

Bayesian Nonparametric Inference - Why and How.

Bayesian Anal 2013 ;8(2)

ICES, University of Texas,

We review inference under models with nonparametric Bayesian (BNP) priors. The discussion follows a set of examples for some common inference problems. The examples are chosen to highlight problems that are challenging for standard parametric inference. Read More

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http://dx.doi.org/10.1214/13-BA811DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3870167PMC
January 2013

Multiple-Shrinkage Multinomial Probit Models with Applications to Simulating Geographies in Public Use Data.

Bayesian Anal 2013 Jun;8(2)

Duke University, Department of Statistical Science.

Multinomial outcomes with many levels can be challenging to model. Information typically accrues slowly with increasing sample size, yet the parameter space expands rapidly with additional covariates. Shrinking all regression parameters towards zero, as often done in models of continuous or binary response variables, is unsatisfactory, since setting parameters equal to zero in multinomial models does not necessarily imply "no effect. Read More

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http://dx.doi.org/10.1214/13-BA816DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3863948PMC

Nonparametric Bayesian Bi-Clustering for Next Generation Sequencing Count Data.

Bayesian Anal 2013 Dec;8(4):759-780

NorthShore University HealthSystem, Chicago, IL, U.S.A.

Histone modifications (HMs) play important roles in transcription through post-translational modifications. Combinations of HMs, known as chromatin signatures, encode specific messages for gene regulation. We therefore expect that inference on possible clustering of HMs and an annotation of genomic locations on the basis of such clustering can contribute new insights about the functions of regulatory elements and their relationships to combinations of HMs. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4523245PMC
December 2013
2 Reads

A Bayesian Dose-finding Design for Drug Combination Trials with Delayed Toxicities.

Authors:
Suyu Liu Jing Ning

Bayesian Anal 2013 Sep 9;8(3):703-722. Epub 2013 Sep 9.

Department of Biostatistics, The University of Texas MD Anderson Cancer Center, TX,

We propose a Bayesian adaptive dose-finding design for drug combination trials with delayed toxicity. We model the dose-toxicity relationship using the Finney model, a model widely used in drug-drug interaction studies. The intuitive interpretations of the Finney model facilitate incorporating the available prior dose-toxicity information from single-agent trials into combination trials through prior elicitation. Read More

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http://dx.doi.org/10.1214/13-BA839DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5136476PMC
September 2013
2 Reads

Nonparametric Bayesian Segmentation of a Multivariate Inhomogeneous Space-Time Poisson Process.

Bayesian Anal 2012 Dec;7(4):813-840

Department of Electrical & Computer Engineering, Duke University, Durham, NC

A nonparametric Bayesian model is proposed for segmenting time-evolving multivariate spatial point process data. An inhomogeneous Poisson process is assumed, with a logistic stick-breaking process (LSBP) used to encourage piecewise-constant spatial Poisson intensities. The LSBP explicitly favors spatially contiguous segments, and infers the number of segments based on the observed data. Read More

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http://dx.doi.org/10.1214/12-BA727DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3670617PMC
December 2012
5 Reads

A Simple Class of Bayesian Nonparametric Autoregression Models.

Bayesian Anal 2013 Mar;8(1):63-88

Pontificia Universidad Católica de Chile, Santiago, CHILE

We introduce a model for a time series of continuous outcomes, that can be expressed as fully nonparametric regression or density regression on lagged terms. The model is based on a dependent Dirichlet process prior on a family of random probability measures indexed by the lagged covariates. The approach is also extended to sequences of binary responses. Read More

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http://dx.doi.org/10.1214/13-BA803DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4454430PMC
March 2013
5 Reads

Flexible Bayesian Human Fecundity Models.

Bayesian Anal 2012 Dec 27;7(4):771-800. Epub 2012 Nov 27.

National Perinatal Epidemiology Unit, University of Oxford, Oxford 327270, U.K.

Human fecundity is an issue of considerable interest for both epidemiological and clinical audiences, and is dependent upon a couple's biologic capacity for reproduction coupled with behaviors that place a couple at risk for pregnancy. Bayesian hierarchical models have been proposed to better model the conception probabilities by accounting for the acts of intercourse around the day of ovulation, i.e. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4926168PMC
December 2012
7 Reads

Prior Effective Sample Size in Conditionally Independent Hierarchical Models.

Bayesian Anal 2012 Sep;7(3)

Department of Biostatistics and Epidemiology, Yokohama City University Medical Center, Yokohama 232-0024, Japan.

Prior effective sample size (ESS) of a Bayesian parametric model was defined by Morita, et al. (2008, , 595-602). Starting with an -information prior defined to have the same means and correlations as the prior but to be vague in a suitable sense, the ESS is the required sample size to obtain a hypothetical posterior very close to the prior. Read More

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http://dx.doi.org/10.1214/12-BA720DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3810292PMC
September 2012
7 Reads

Commensurate Priors for Incorporating Historical Information in Clinical Trials Using General and Generalized Linear Models.

Bayesian Anal 2012 Aug;7(3):639-674

Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, 55455, USA.

Assessing between-study variability in the context of conventional random-effects meta-analysis is notoriously difficult when incorporating data from only a small number of historical studies. In order to borrow strength, historical and current data are often assumed to be fully homogeneous, but this can have drastic consequences for power and Type I error if the historical information is biased. In this paper, we propose empirical and fully Bayesian modifications of the commensurate prior model (Hobbs et al. Read More

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http://dx.doi.org/10.1214/12-BA722DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4007051PMC
August 2012
3 Reads

A Bayesian Semiparametric Temporally-Stratified Proportional Hazards Model with Spatial Frailties.

Bayesian Anal 2011 ;6(4):1-48

Department of Statistics, University of South Carolina, Columbia, SC 29208 ( ).

Incorporating temporal and spatial variation could potentially enhance information gathered from survival data. This paper proposes a Bayesian semiparametric model for capturing spatio-temporal heterogeneity within the proportional hazards framework. The spatial correlation is introduced in the form of county-level frailties. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3255564PMC
January 2011
6 Reads

Rejoinder.

Bayesian Anal 2012;7(4):809-812. Epub 2012 Oct 27.

National Perinatal Epidemiology Unit, University of Oxford, Oxford 327270, U.K.

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http://dx.doi.org/10.1214/12-BA726REJDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4774643PMC
October 2012
4 Reads

Bayesian Dose Finding for Combined Drugs with Discrete and Continuous Doses.

Bayesian Anal 2012 ;7(4):1035-1052

Novartis Pharmaceuticals Corporation.

The trend of treating patients with combined drugs has grown in cancer clinical trials. Often, evaluating the synergism of multiple drugs is the primary motivation for such drug-combination studies. To enhance the patient response, a new cancer therapeutic agent is often investigated together with an existing standard of care (SOC) agent. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3745226PMC
January 2012
2 Reads

Simultaneous Bayesian inference for skew-normal semiparametric nonlinear mixed-effects models with covariate measurement errors.

Bayesian Anal 2012 Jan 11;7(1):189-210. Epub 2011 Mar 11.

Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, FL.

Longitudinal data arise frequently in medical studies and it is a common practice to analyze such complex data with nonlinear mixed-effects (NLME) models which enable us to account for between-subject and within-subject variations. To partially explain the variations, covariates are usually introduced to these models. Some covariates, however, may be often measured with substantial errors. Read More

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http://dx.doi.org/10.1214/12-BA706DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3584628PMC
January 2012
4 Reads

Hierarchical Bayesian nonparametric mixture models for clustering with variable relevance determination.

Bayesian Anal 2011 Jul;6(2):329-352

Department of Statistics, University of Oxford, Oxford, U.K.,

We propose a hierarchical Bayesian nonparametric mixture model for clustering when some of the covariates are assumed to be of varying relevance to the clustering problem. This can be thought of as an issue in variable selection for unsupervised learning. We demonstrate that by defining a hierarchical population based nonparametric prior on the cluster locations scaled by the inverse covariance matrices of the likelihood we arrive at a 'sparsity prior' representation which admits a conditionally conjugate prior. Read More

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http://dx.doi.org/10.1214/11-BA612DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3121559PMC
July 2011
1 Read

Nonparametric Bayesian models through probit stick-breaking processes.

Bayesian Anal 2011 Mar;6(1)

Department of Statistics, Duke University, Durham, NC.

We describe a novel class of Bayesian nonparametric priors based on stick-breaking constructions where the weights of the process are constructed as probit transformations of normal random variables. We show that these priors are extremely flexible, allowing us to generate a great variety of models while preserving computational simplicity. Particular emphasis is placed on the construction of rich temporal and spatial processes, which are applied to two problems in finance and ecology. Read More

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http://dx.doi.org/10.1214/11-BA605DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3865248PMC
March 2011
2 Reads

Spatial Mixture Modelling for Unobserved Point Processes: Examples in Immunofluorescence Histology.

Bayesian Anal 2009 Dec;4(2):297-316

Department of Statistical Science, Duke University, Durham, NC.

We discuss Bayesian modelling and computational methods in analysis of indirectly observed spatial point processes. The context involves noisy measurements on an underlying point process that provide indirect and noisy data on locations of point outcomes. We are interested in problems in which the spatial intensity function may be highly heterogenous, and so is modelled via flexible nonparametric Bayesian mixture models. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2965046PMC
December 2009
2 Reads

Selection Sampling from Large Data Sets for Targeted Inference in Mixture Modeling.

Bayesian Anal 2010 ;5(3):1-22

Department of Statistical Science, Duke University, Durham, NC,

One of the challenges in using Markov chain Monte Carlo for model analysis in studies with very large datasets is the need to scan through the whole data at each iteration of the sampler, which can be computationally prohibitive. Several approaches have been developed to address this, typically drawing computationally manageable subsamples of the data. Here we consider the specific case where most of the data from a mixture model provides little or no information about the parameters of interest, and we aim to select subsamples such that the information extracted is most relevant. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2943396PMC
January 2010

A Bayesian Image Analysis of Radiation Induced Changes in Tumor Vascular Permeability.

Bayesian Anal 2010 ;5(1):189-212

Pfizer Inc., New York, NY 10017.

This work is motivated by a quantitative Magnetic Resonance Imaging study of the relative change in tumor vascular permeability during the course of radiation therapy. The differences in tumor and healthy brain tissue physiology and pathology constitute a notable feature of the image data-spatial heterogeneity with respect to its contrast uptake profile (a surrogate for permeability) and radiation induced changes in this profile. To account for these spatial aspects of the data, we employ a Gaussian hidden Markov random field (MRF) model. Read More

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http://dx.doi.org/10.1214/10-BA508DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2864505PMC
January 2010
2 Reads

Bayesian Variable Selection and Computation for Generalized Linear Models with Conjugate Priors.

Bayesian Anal 2008 Jul;3(3):585-614

Department of Statistics, University of Connecticut, Storrs, CT, http://www.stat.uconn.edu/~mhchen.

In this paper, we consider theoretical and computational connections between six popular methods for variable subset selection in generalized linear models (GLM's). Under the conjugate priors developed by Chen and Ibrahim (2003) for the generalized linear model, we obtain closed form analytic relationships between the Bayes factor (posterior model probability), the Conditional Predictive Ordinate (CPO), the L measure, the Deviance Information Criterion (DIC), the Aikiake Information Criterion (AIC), and the Bayesian Information Criterion (BIC) in the case of the linear model. Moreover, we examine computational relationships in the model space for these Bayesian methods for an arbitrary GLM under conjugate priors as well as examine the performance of the conjugate priors of Chen and Ibrahim (2003) in Bayesian variable selection. Read More

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http://dx.doi.org/10.1214/08-BA323DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2680310PMC
July 2008
3 Reads

Spiked Dirichlet Process Prior for Bayesian Multiple Hypothesis Testing in Random Effects Models.

Bayesian Anal 2009 ;4(4):707-732

Department of Biostatistics, University of Michigan, Ann Arbor, MI,

We propose a Bayesian method for multiple hypothesis testing in random effects models that uses Dirichlet process (DP) priors for a nonparametric treatment of the random effects distribution. We consider a general model formulation which accommodates a variety of multiple treatment conditions. A key feature of our method is the use of a product of spiked distributions, i. Read More

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http://dx.doi.org/10.1214/09-BA426DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3741668PMC
January 2009
2 Reads

Misinformation in the conjugate prior for the linear model with implications for free-knot spline modelling.

Bayesian Anal 2006 ;1(2):375-383

Department of Biostatistics, Harvard School of Public Health, Boston, MA, http://www.biostat.harvard.edu/~paciorek.

In the conjugate prior for the normal linear model, the prior variance for the coefficients is a multiple of the error variance parameter. However, if the prior mean for the coefficients is poorly chosen, the posterior distribution of the model can be seriously distorted because of prior dependence between the coefficients and error variance. In particular, the error variance will be overestimated, as will the posterior variance of the coefficients. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2186203PMC
January 2006
2 Reads

Semi-parametric Bayesian Inference for Multi-Season Baseball Data.

Bayesian Anal 2008 ;3(2):317-338

Departamento de Estadística, Pontificia Universidad Católica de Chile, Santiago, CHILE.

We analyze complete sequences of successes (hits, walks, and sacrifices) for a group of players from the American and National Leagues, collected over 4 seasons. The goal is to describe how players' performances vary from season to season. In particular, we wish to assess and compare the effect of available occasion-specific covariates over seasons. Read More

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http://dx.doi.org/10.1214/08-BA312DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3168950PMC
January 2008
6 Reads

Bayesian Hierarchical Multiresolution Hazard Model for the Study of Time-Dependent Failure Patterns in Early Stage Breast Cancer.

Bayesian Anal 2007 May;2(3):591-610

Department of Health Studies, University of Chicago, Chicago, IL,

The multiresolution estimator, developed originally in engineering applications as a wavelet-based method for density estimation, has been recently extended and adapted for estimation of hazard functions (Bouman et al. 2005, 2007). Using the multiresolution hazard (MRH) estimator in the Bayesian framework, we are able to incorporate any a priori desired shape and amount of smoothness in the hazard function. Read More

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http://dx.doi.org/10.1214/07-BA223DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3056202PMC
May 2007
1 Read

Loss Function Based Ranking in Two-Stage, Hierarchical Models.

Bayesian Anal 2006 Jan;1(4):915-946

National Institute of Environmental Health Science, Research Triangle Park, NC,

Performance evaluations of health services providers burgeons. Similarly, analyzing spatially related health information, ranking teachers and schools, and identification of differentially expressed genes are increasing in prevalence and importance. Goals include valid and efficient ranking of units for profiling and league tables, identification of excellent and poor performers, the most differentially expressed genes, and determining "exceedances" (how many and which unit-specific true parameters exceed a threshold). Read More

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http://projecteuclid.org/euclid.ba/1340370947
Publisher Site
http://dx.doi.org/10.1214/06-BA130DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2896056PMC
January 2006
7 Reads
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