Bayesian Inference (Working group)
Bayesian methods are becoming increasingly popular in the academic and
practitioner communities because of the recent development of techniques like
Markov chain Monte Carlo (MCMC) simulation. The Bayesian paradigm is an attempt to utilize all available
information in decision-making. Prior knowledge coming from experience, expert judgment, or previously
collected data is used with current data to characterize the current state of knowledge. These methods allow
the use of models of complex physical phenomena that were previously too difficult to estimate. Bayesian methods
offer a means of more fully understanding issues that are central to many practical problems by allowing
researchers to build integrated models of behavior that can be estimated with limited amounts of data.
Biostatistical applications (Working group)
Bayesian networks with their associated methods have now been around in biomedical fields for more than a
decade. They have become increasingly popular for representing and handling uncertain knowledge in biological
phenomena. Almost simultaneously, the use of Bayesian methods in biology has increased in popularity. Moreover,
interest in Bayesian methods is emerging within bioinformatics, e.g. for building models for protein structure
prediction and the interpretation of microarray gene expression data. Bayesian models are used in medicine to
assist in the diagnosis of disorders and to predict the natural course of disease or outcome after treatment
(prognosis). In the context of medical decision making, Bayesian models can be easily integrated with decision
theory to yield models for the selection of optimal treatments, or to develop models for health-care planning
under uncertainty.
Environmental applications (Working group)
Most physical processes exhibit important spatial-temporal variability that needs to be characterized, in
conjunction with dependencies on explanatory variables, and hierarchical Bayesian statistical modeling offers
a natural framework and a very powerful means for representing complex global phenomena through a series of
simple local structures. This type of research on Bayesian statistics for environmental data requires a good
understanding of the processes involved in the particular problem, of the sources of data needed, a background
in Bayesian statistics, and an interest in challenging multi-disciplinary problems. This implies active
collaboration with non-statisticians, i.e. climatologists, ecologists and environmental scientists.
For a complete list of my pubshlied work please see my Resume
Articles submitted for publications
Kalaylioglu, Z. I. and Ghosh, S. K. Bayesian Unit-root Tests for Stochastic Volatility Models
(tentatively accepted)
Wang, D., Pantula, S. G. and Ghosh, S. K. Maximum Likelihood Estimation and Unit Root Test for First Order
Random Coefficient Autoregressive Models (revision submitted).
Devineni, N., Sankarasubramanian, A. and Ghosh, S. K. Multi-model Ensembling of Probabilistic Streamflow
Forecasts: Role of Predictor State Space in Skill Evaluation (revision submitted).
White, G. and Ghosh, S. K. A Stochastic neighborhood Conditional Auto-Regressive Model for Spatial Data
(revision submitted).
Huggins, W., Ghosh, S. K., Shapkina, T., Nanda, K. and Wollenzien. RNA Photocrosslinking is restricted by
conformational flexibility - implications for measuring conformational energy (revision submitted).
Ravindran, P. and Ghosh, S. K. Bayesian Analysis of Circular Data Using Wrapped Distributions
(revision in progress).
Ghosh, S. K., Lee, H., Davis, J. and Bhave, P. Spatio-temporal Analysis of Total Nitrate Concentrations
Using Dynamic Statistical Models (revision in progress).
Park, J., Genton, M. and Ghosh, S. K. Nonparametric Autocovariance Estimation from Censored Time Series
(revision in progress).
Wang, D. and Ghosh, S.K. Bayesian Analysis of Random Coefficient Autoregressive Models (revision in progress).
Kyung, M. and Ghosh, S, K. Maximum Likelihood Estimation for Directional Conditionally Autoregressive Models
(revision in progress).
Kyung, M. and Ghosh, S. K. Bayesian Inference for Directional Conditionally Autoregressive Models
(revision in progress).
Curtis, S. M. and Ghosh, S. K. A Variable Selection Approach to Bayesian Monotonic Regression with Bernstein
Polynomials (in review).
Curtis, S. M. and Ghosh, S. K. A Bayesian Approach to Multicollinearity and the Simultaneous Selection and
Clustering of Predictors in Linear Regression (in review).
Belasco, E., Goodwin, B. K. Goodwin, Ghosh, S. K. A Multivariate Evaluation of Ex-ante risks Associated with
Fed Cattle Production (in review).
Mishra, K. and Ghosh, S. K. Bayesian regression Models for the Quality Adjusted Lifetime Data with Zero
Time Duration Health States (in review).
Zheng, J., Frey, C. and Ghosh, S. K. Dealing with Incomplete Data and dependencies among Sampling
Distributions in Probabilistic Human Helth Exposure Assessment: Methodology and Case Study (in review).
Goyal, L. and Ghosh, S. K. Statistical Inference for Nonlinear Models Involving Ordinary Differential
Equations (in review).
Anand, S. and Ghosh, S. K. A Bayesian Approach to Assessing the Risk of QT Prolongation (in review).
Hughes-Oliver, J. M., Heo, T. Y. and Ghosh, S. K. An Autoregressive Point Source Model for Spatial
Processes (in review).
Gosky, R. and Ghosh, S. K. A Comparative Study of Bayesian Model Selection Criteria for Capture-Recapture
Models for Closed Populations (in review)
Suraj Anand, Novel Statistical Approaches for Planning
and Investigating the Risk of QT Prolongation in "thorough QTc studies",
North Carolina State University.
Arun Krishna, New Penalization Methods for Variable Selection for Mixed Effects
Models, North Carolina State University. (co-advisor Dr. H. Bondell)
Haojun Ouyang, Bayesian Methods to Control FDR within GWAS, North Carolina State University. (co-adviser Dr. J. Tzeng)
Ying Zhu, Modeling Dependence in the Design of Crop Insurance,
North Carolina State University. (co-advisor Dr. B. Goodwin)
Carl Dicasoli, Modeling Crossing Survival Functions, North Carolina State University.
(co-adviser Dr. S. Ghosal)
Elizabeth Nelson, Analyzing Cure Rates using Accelerated Failure Time Models,
North Carolina State University. (co-advisor Dr. W. Lu)
Ani Eloyan, New Methods for Independent Component Analysis,
North Carolina State University.
S. McKay Curtis, Variable Selection Methods with Applications to Shape Restricted
Regression,
North Carolina State University. (co-advisor Dr. S. Ghoshal)
