122 citations · 159 across the 15 of their papers we have counts for
15 papers
Uncertainty Quantification Via the Posterior Predictive Variance
Sanjay Chaudhuri, Dean Dustin, Bertrand Clarke
We use the law of total variance to generate multiple expansions for the posterior predictive variance. These expansions are sums of terms involving conditional expectations and co…
elhmc: An R Package for Hamiltonian Monte Carlo Sampling in Bayesian Empirical Likelihood
Dang Trung Kien, Neo Han Wei, Sanjay Chaudhuri
In this article, we describe a {\tt R} package for sampling from an empirical likelihood-based posterior using a Hamiltonian Monte Carlo method. Empirical likelihood-based methodol…
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 Unified Statistical Procedure to Analyse Irreversible Thermal Curves
Sanjay Chaudhuri, Dang Trung Kien, Gopinatha Suresh Kumar +3
DNA hybridisation experiments are crucial for studying the thermodynamic and kinetic profiles of various systems in nucleic acid chemistry. The phenomenon of hysteresis is commonly…
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…