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Department of Statistics


Latent Variables 2016

The University of South Carolina Department of Statistics hosted the Latent Variables Conference 2016 on October 14-16.

 

Conference Description 
 

Conference Speakers »

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Plenary Speakers

David Blei, Columbia University
Scaling and generalizing variational inference, with examples from Bayesian nonparametrics

Xuming He, University of Michigan
From latent variable and mixture models to inference in subgroup analysis

Sonia Petrone, Universita' Bocconi
A review of Bayesian nonparametric regression through mixture models

Yanyuan Ma, Penn State University
Functional and very high dimension reduction
 

Presenters

Le Bao, Penn State University
Identification of endogenous retrovirus integration sites using a mixture model

Anindya Bhadra, Purdue University
Prediction risk for global-local shrinkage regression

Tamara Broderick, Massachusetts Institute of Technology
Edge-exchangeable graphs, sparsity, and power laws

Paramita Chakraborty, University of South Carolina
A modified mixed model approach to the large scale multiple testing problem

Miguel de Carvalho, University of Edinburgh
Discrimination surfaces for region-specific brain asymmetry analysis brain asymmetry analysis

Amanda Fairchild, University of South Carolina
Frailty models in the social sciences

Colin Gallagher, Clemson University
A general framework for the regression analysis of pooled biomarker assessments

Subhashis Ghoshal, North Carolina State University
Multiple testing approaches for removing background noise from images

Chris Hans, Ohio State University
Latent-Variable approaches for accurate computation in Bayesian scale-usage models

Jeffrey Hart, Texas A & M University
A nonparametric goodness-of-fit test for random effects models via cross-validation Bayesian factors

Vanda Inacio de Carvalho, University of Edinburgh
Nonparametric Bayesian regression analysis of the Youden index

Inyoung Kim, Virginia Tech
Functional semiparametric Bayesian time varying coeffcient ME models in matched case-crossover studies

Yehua Li, Iowa State University
Generalized linear mixed models with Gaussian mixture random effects: an application to nationwide kidney transplant center evaluation

Haiqun Lin, Yale University

Xiaoyan (Iris) Lin, University of South Carolina
Modeling rater diagnostic skills in binary classification processes

Jinchi Lv, University of Southern California
Latent variable augmented sparse regression

Robert Lyles, Emory University
Adjusting for processing or measurement error in regression analyses with biomarker exposure levels assessed on pooled samples

Shuangge Ma, Yale University
Accounting for measurement uncertainty in environmental preterm studies

Arnab Maity, North Carolina State University
Association study of children's methylation and growth trajectory using functional mixed models

Yaakov Malinovsky, University of Maryland, Baltimore County
Revisiting nested group testing procedures: new results, comparisons, robustness

Amita Manatunga, Emory University
A latent class modeling approach for predicting disease status using functional data in the absence of a gold standard

Ryan Martin, University of Illinois, Chicago
A double empirical Bayes approach for high-dimensional problems

Alberto Maydeu-Olivares, University of South Carolina
IRT modeling of ordinal forced-choice data

Chris McMahan, Clemson University
Covariate adjusted measures of diagnostic accuracy based on pooled biomarkers

Kerrie Nelson, Boston University
Modeling agreement between multiple raters' ordinal classifications

Debdeep Pati, Florida State University
Bayesian community detection with unknown number of communities

Edsel Peña, University of South Carolina
Professor Jayaram Sethuraman: teacher, father figure, friend, and colleague

James S. Roberts, Georgia Tech
The multidimensional generalized graded unfolding model: issues and applications

Veronika Ročková, University of Chicago
Fast Bayesian factor analysis via automatic rotations to sparsity

Louis Roussos, Measured Progress
A cautionary tale on equating

Jayaram Sethuraman, Florida State University
Latent story of the stick breaking representation for the Dirichlet process

Samiran Sinha, Texas A&M University
Analysis of proportional odds models with censoring and errors-in-covariates

Elizabeth Slate, Florida State University

Dongchu Sun, University of Missouri
An objective prior for hyperparameters in normal hierarchical models

Jianguo (Tony) Sun, University of Missouri
Regression analysis of informatively interval-censored failure time data

Robert Taylor, Clemson University
J. Sethuraman, the man and the scholar

Sherry Wang, Southern Methodist University
A Bayesian latent variable approach to aggregation of top-ranked partial gene lists in genomic studies

Grace Yi, University of Waterloo
Variable selection for longitudinal data analysis in the presence of missing observations and measurement error

Haiming Zhou, Northern Illinois University
Bayesian semiparametric models for spatially correlated arbitrarily censored data

Mingyuan Zhou, University of Texas, Austin
Permuted and augmented stick-breaking multinomial regression

  

The organizers are grateful for the co-sponsorship of the National Institute of Statistical Sciences (NISS) and the University of South Carolina Office of the Vice President for Research.  We are also grateful to the National Science Foundation (NSF) for providing funding for travel awards for graduate students and junior researchers.  Conference attendees from NISS affiliates (see NISS Affiliate Information) can take advantage of the Affiliate Award Fund (see link for Affiliate reimbursement form).

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