122 citations · 159 across the 15 of their papers we have counts for
6 papers · 1 filter
Population level information combined parameter estimation from complex survey datasets
Sanjay Chaudhuri, Mark S. Handcock, Michael S. Rendall
We consider an empirical likelihood framework for inference for a statistical model based on an informative sampling design and population-level information. The population-level i…
A Two-step Metropolis Hastings Method for Bayesian Empirical Likelihood Computation with Application to Quantile Regression and Bayesian Model Selection
Sanjay Chaudhuri, Teng Yin, Snehashis Chakraborty +1
Empirical likelihood-based methods have been used under the Bayesian framework (BayesEL) in recent times. For statistical inference, these methods require efficient Markov chain Mo…
General Unbiased Estimating Equations for Variance Components in Linear Mixed Models
Tatsuya Kubokawa, Shonosuke Sugasawa, Hiromasa Tamae +1
This paper introduces a general framework for estimating variance components in the linear mixed models via general unbiased estimating equations, which include some well-used esti…
On a Variational Approximation based Empirical Likelihood ABC Method
Sanjay Chaudhuri, Subhroshekhar Ghosh, David J. Nott +1
Many scientifically well-motivated statistical models in natural, engineering, and environmental sciences are specified through a generative process. However, in some cases, it may…
A Conditional Empirical Likelihood Based Method for Model Parameter Estimation from Complex survey Datasets
Sanjay Chaudhuri, Mark S. Handcock
We consider an empirical likelihood framework for inference for a statistical model based on an informative sampling design. Covariate information is incorporated both through the…
Reversing the Stein Effect
Michael D. Perlman, Sanjay Chaudhuri
The Reverse Stein Effect is identified and illustrated: A statistician who shrinks his/her data toward a point chosen without reliable knowledge about the underlying value of the p…