Bayesian inference for hidden truncation pareto (II) models

3^rd International Conference & Exhibition on Biometrics & Biostatistics

October 20-21, 2014 DoubleTree by Hilton Baltimore - BWI Airport, USA

Indranil Ghosh and Ayman Alzaatreh

Scientific Tracks Abstracts: J Biom Biostat

Abstract :

Data from a hidden truncation Pareto (II) distribution are to be used to make inferences about the inequality, precision and truncation parameters. Two different types of dependent prior analyses are reviewed and compared with each other. It is argued that mathematical tractability should be, perhaps, a minor consideration in choosing the appropriate choices of the hyperparameters for the prior densities. Some illustrative examples are provided.

Biography :

Indranil Ghosh received his PhD in Applied Statistics from the University of California, Riverside. After that he went to work as a visiting Assistant Professor of Statistics at the North Dakota State University. Currently he works as an Assistant Professor of Statistics at the Austin Peay State University in the Department of Mathematics and Statistics. He is an Editorial Board Member of the International Journal of Theoretical and Computational Statistics. His current research focuses on distribution theory, characterizing bivariate and multivariate probability distribution, hidden truncation models and its application and associated inference in both classical and Bayesian paradigm.