S. MacEachern

发表

Estimating mixture of dirichlet process models

S. MacEachern, P. Müller, 1998 .

Sequential importance sampling for nonparametric Bayes models: The next generation

Jun S. Liu, S. MacEachern, M. Clyde, 1999 .

An ANOVA Model for Dependent Random Measures

S. MacEachern, P. Müller, M. De Iorio, 2004 .

Bayesian Nonparametric Spatial Modeling With Dirichlet Process Mixing

S. MacEachern, A. Gelfand, A. Kottas, 2005 .

A semiparametric Bayesian model for randomised block designs

S. MacEachern, Christopher A. Bush, 1996 .

Decision Theoretic Aspects of Dependent Nonparametric Processes

S. MacEachern, 2000 .

Bayesian Density Estimation and Inference Using Mixtures

S. MacEachern, P. Müller, 2007 .

Computational Methods for Mixture of Dirichlet Process Models

Steven N. MacEachern, S. MacEachern, 1998 .

Estimating normal means with a conjugate style dirichlet process prior

S. MacEachern, 1994 .

A nonparametric Bayesian model for inference in related longitudinal studies

S. MacEachern, P. Müller, G. Rosner, 2005 .

Regularization of Case-Specific Parameters for Robustness and Efficiency

S. MacEachern, Yoonkyung Lee, Yoonsuh Jung, 2012, 1210.0701.

Case-deletion importance sampling estimators: Central limit theorems and related results

S. MacEachern, M. Peruggia, I. Epifani, 2008, 0807.0725.

Generalized Poststratification and Importance Sampling for Subsampled Markov Chain Monte Carlo Estimation

Subharup Guha, Steven N. MacEachern, S. MacEachern, 2006 .

Benchmark Estimation for Markov chain Monte Carlo Samples

S. MacEachern, M. Peruggia, Subharup Guha, 2004 .

Importance Link Function Estimation for Markov Chain Monte Carlo Methods

Mario Peruggia, Steven N. MacEachern, S. MacEachern, 2000 .

Aperiodic Chaotic Orbits

L. Mark Berliner, Steven N. MacEachern, L. M. Berliner, 1993 .

Efficient quantile regression for heteroscedastic models

S. MacEachern, Yoonkyung Lee, Yoonsuh Jung, 2014, Journal of Statistical Computation and Simulation.

A Probit Model with Structured Covariance for Similarity Effects and Source of Volume Calculations

Greg M. Allenby, Jeffrey P. Dotson, Jeff D. Brazell, 2015 .

Bayesian variable selection for proportional hazards models

S. MacEachern, J. Ibrahim, Ming-Hui Chen, 1999 .

Shape-constrained semiparametric additive stochastic volatility models

S. MacEachern, P. Craigmile, Jiangyong Yin, 2019, Statistical Theory and Related Fields.

Classification via kernel product estimators

Steven N. MacEachern, Craig A. Cooley, Craig Allen Cooley, 1998 .

Bayesian Restricted Likelihood Methods: Conditioning on Insufficient Statistics in Bayesian Regression

S. MacEachern, Yoonkyung Lee, John Lewis, 2018, Bayesian Analysis.

A class of generalized linear mixed models adjusted for marginal interpretability

Jeffrey J Gory, S. MacEachern, P. Craigmile, 2020, Statistics in medicine.

Bandwidth Selection for Kernel Density Estimation with a Markov Chain Monte Carlo Sample

Steven N. MacEachern, Yoonsuh Jung, Hang J. Kim, 2016, 1607.08274.

The Generalized Multiset Sampler

S. MacEachern, H. J. Kim, 2015 .

Subsampling the Gibbs sampler: variance reduction

Mario Peruggia, Steven N. MacEachern, S. MacEachern, 2000 .

Bayesian Models for Non‐linear Autoregressions

Peter Müller, Steven N. MacEachern, Michael A. West, 1997 .

Covariance Decompositions for Accurate Computation in Bayesian Scale-Usage Models

Greg M. Allenby, Peter F. Craigmile, Xinyi Xu, 2012 .

Discussion on the paper by Kong, McCullagh, Meng, Nicolae and Tan

S. MacEachern, J. Robins, Y. Ritov, 2003 .

Modified check loss for efficient estimation via model selection in quantile regression

S. MacEachern, Yoonsuh Jung, Hang Joon Kim, 2020, Journal of applied statistics.

Benchmark Estimation for Markov Chain

S. MacEachern, M. Peruggia, Subharup Guha, 2016 .

Nonparametric Bayesian methods: a gentle introduction and overview

S. MacEachern, 2016 .

Parametric and semiparametric hypotheses in the linear model

Subharup Guha, Steven N. MacEachern, S. MacEachern, 2011 .

Local-Mass Preserving Prior Distributions for Nonparametric Bayesian Models

S. MacEachern, G. Mills, Yiling Lu, 2014 .

Block Hyper-g Priors in Bayesian Regression

S. MacEachern, Chris Hans, Agniva Som, 2014, 1406.6419.

Minimally informative prior distributions for non‐parametric Bayesian analysis

S. MacEachern, Christopher A. Bush, Juhee Lee, 2010 .

Sequential sampling models of choice: Some recent advances

James T. Townsend, Adele Diederich, Greg M. Allenby, 2008 .

The Dependent Poisson Race Model and Modeling Dependence in Conjoint Choice Experiments

Angela M. Dean, Thomas Otter, Steven N. MacEachern, 2008 .

A Probit Model with Structured Covariance for Similarity Effects and Source of Volume Calculations

Greg M. Allenby, Jeffrey P. Dotson, Jeff D. Brazell, 2015 .

Asymptotic inference for dynamical systems observed with error

L. Mark Berliner, Steven N. MacEachern, L. M. Berliner, 1995 .

Nonparametric Bayesian modelling for item response

Steven N. MacEachern, S. MacEachern, Kristin A. Duncan, 2008 .

Transformations and Bayesian density estimation

S. MacEachern, Xinyi Xu, Andrew Bean, 2016 .

Marginally Interpretable Generalized Linear Mixed Models

Jeffrey J Gory, S. MacEachern, P. Craigmile, 2016, 1610.01526.

Bayesian Synthesis

S. MacEachern, M. Peruggia, Qingzhao Yu, 2007 .

7th Workshop on Bayesian Nonparametrics

S. MacEachern, A. V. D. Vaart, J. Rousseau, 2009 .

Low cancer incidence rates in Ohio Amish

S. MacEachern, C. Bloomfield, S. Lemeshow, 2009, Cancer Causes & Control.

Reconciling Curvature and Importance Sampling Based Procedures for Summarizing Case Influence in Bayesian Models

S. MacEachern, M. Peruggia, Z. Thomas, 2018, Journal of the American Statistical Association.

On the two-dataset problem.

S. MacEachern, Koji Miyawaki, 2019 .

Rediscovering a little known fact about the t-test and the F-test: Algebraic, Geometric, Distributional and Graphical Considerations

S. MacEachern, M. Peruggia, J. Sinnott, 2019, 1907.08703.

Introduction to Statistical Thought

S. MacEachern, 2006 .

Rediscovering a little known fact about the t-test: algebraic, geometric, distributional and graphical considerations

S. MacEachern, M. Peruggia, J. Sinnott, 2019, 1907.08703.

Consistency of Bayes estimators without the assumption that the model is correct

S. MacEachern, Juhee Lee, 2011 .

Effect of surgical site infection on survival after limb amputation in the curative‐intent treatment of canine appendicular osteosarcoma: a Veterinary Society of Surgical Oncology retrospective study

S. MacEachern, J. Kirpensteijn, Ameet Singh, 2018, Veterinary surgery : VS.

Calibrated Bayes factors for model comparison

S. MacEachern, X. Xu, P. Lu, 2019, Journal of Statistical Computation and Simulation.

An evaluation of bayes posterior probability regions for a survival curve

S. MacEachern, 1993 .

Assessing Convergence and Mixing of MCMC Implementations via Stratification

L. Mark Berliner, Steven N. MacEachern, L. M. Berliner, 2012 .

Efficient MCMC Schemes for Robust Model Extensions Using Encompassing Dirichlet Process Mixture Models

Steven N. MacEachern, Peter Müller, S. MacEachern, 2000 .

Bayesian Nonparametric Survival Analysis: A Comparison of the Kaplan-Meier and Berliner-Hill Estimators

Steven N. MacEachern, Bruce M. Hill, S. MacEachern, 1992 .

Clustered Bayesian Model Averaging

Qingzhao Yu, Mario Peruggia, Steven N. MacEachern, 2013 .

The Dependent Dirichlet Process and Related Models

Steven N. MacEachern, Alejandro Jara, Peter Mueller, 2020, Statistical Science.

Inference functions in high dimensional Bayesian inference

Juhee Lee, Steven N. MacEachern, S. MacEachern, 2014 .

Comment on article by Jain and Neal

S. MacEachern, 2007 .

Restricting exchangeable nonparametric distributions

Eric P. Xing, Sinead Williamson, Steve N. MacEachern, 2012, NIPS.

Toward Rational Social Decisions: A Review and Some Results

Joseph B. Kadane, Steven N. MacEachern, S. MacEachern, 2014 .

SPATIAL NONPARAMETRIC BAYESIAN MODELS

Alan E. Gelfand, Athanasios Kottas, Steven N. MacEachern, 2001 .

A Robust-Likelihood Cumulative Sum Chart

Youlan Rao, S. MacEachern, Y. Rao, 2007 .

A regression approach to the two-dataset problem

Koji Miyawaki, Steven N. MacEachern, S. MacEachern, 2019, Statistics.

Empirical likelihood for the analysis of experimental designs

S. MacEachern, M. Peruggia, Eunseop Kim, 2021, Journal of Nonparametric Statistics.

Economic variable selection

Koji Miyawaki, Steven N. MacEachern, S. MacEachern, 2019, Canadian Journal of Statistics.

Judgement Post‐Stratification with Imprecise Rankings

Douglas A Wolfe, Elizabeth A Stasny, S. MacEachern, 2004, Biometrics.

Bayesian Tools for EDA and Model Building: A Brainy Study

Mario Peruggia, Steven N. MacEachern, S. MacEachern, 2002 .

Robust Inference via the Blended Paradigm

John Lewis, Yoonkyung Lee, Steven MacEachern, 2012 .

Subsampling the Gibbs Sampler

L. Mark Berliner, Steven N. MacEachern, S. MacEachern, 1994 .

The Dependent Poisson Race Model and Modeling Dependence in Conjoint Choice Experiments

S. MacEachern, A. Dean, Thomas Otter, 2007 .

Sequential importance sampling for

Jun S. Liu, Steven N. MacEachern, Merlise A. Clyde, 1999 .

Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments

Hang J. Kim, S. MacEachern, C. Hong, 2021, 2110.10604.

melt: Multiple Empirical Likelihood Tests in R

S. MacEachern, M. Peruggia, Eunseop Kim, 2024, J. Stat. Softw..

Familial inference: Tests for hypotheses on a family of centres

Catherine S. Forbes, S. MacEachern, M. Peruggia, 2022, Biometrika.

Superset model problem

S. MacEachern, Koji Miyawaki, 2022, 2209.14555.

Bayesian Synthesis: Combining subjective analyses, with an application to ozone data

Qingzhao Yu, Mario Peruggia, Steven N. MacEachern, 2011, 1107.5935.

A Process of Dependent Quantile Pyramids

S. MacEachern, Hyoin An, 2023, 2306.02126.

A robust latent CUSUM chart for monitoring customer attrition

S. MacEachern, Chunjie Wu, J. Schneider, 2022, Journal of applied statistics.

ANOVA DDP Models: A Review

Peter Müller, Maria De Iorio, Steven N. MacEachern, 2003 .

Landmark-Constrained Elastic Shape Analysis of Planar Curves

Sebastian Kurtek, Justin Strait, Steven N. MacEachern, 2017 .

A conditional Lindley paradox in Bayesian linear models

S. MacEachern, Chris Hans, Agniva Som, 2016 .