Subharup Guha

Bayesian Hidden Markov Modeling of Array CGH Data (2006)

Genomic alterations have been linked to the development and progression of cancer. The technique of Comparative Genomic Hybridization (CGH) yields data consisting of fluorescence intensity ratios of...

Bayesian Hidden Markov Modeling of Array CGH Data (2006)

Genomic alterations have been linked to the development and progression of cancer. The technique of Comparative Genomic Hybridization (CGH) yields data consisting of fluorescence intensity ratios of...

Posterior Simulation in the Generalized Linear Model with Semiparmetric Random Effects (2006)

Genomic alterations have been linked to the development and progression of cancer. The technique of Comparative Genomic Hybridization (CGH) yields data consisting of fluorescence intensity ratios of...

Posterior Simulation in the Generalized Linear Model with Semiparmetric Random Effects (2006)

Generalized linear mixed models with semiparametric random effects are useful in a wide variety of Bayesian applications. When the random effects arise from a mixture of Dirichlet process (MDP)...

Posterior Simulation in the Generalized Linear Model with Semiparmetric Random Effects (2006)

Generalized linear mixed models with semiparametric random effects are useful in a wide variety of Bayesian applications. When the random effects arise from a mixture of Dirichlet process (MDP)...

Posterior Simulation in the Generalized Linear Model with Semiparmetric Random Effects (2006)

Generalized linear mixed models with semiparametric random effects are useful in a wide variety of Bayesian applications. When the random effects arise from a mixture of Dirichlet process (MDP)...

Gauss-Seidel Estimation of Generalized Linear Mixed Models with Application to Poisson Modeling of Spatially Varying Disease Rates (2005)

Generalized linear mixed models with semiparametric random effects are useful in a wide variety of Bayesian applications. When the random effects arise from a mixture of Dirichlet process (MDP)...

Gauss-Seidel Estimation of Generalized Linear Mixed Models with Application to Poisson Modeling of Spatially Varying Disease Rates (2005)

Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures...

Gauss-Seidel Estimation of Generalized Linear Mixed Models with Application to Poisson Modeling of Spatially Varying Disease Rates (2005)

Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures...

Gauss-Seidel Estimation of Generalized Linear Mixed Models with Application to Poisson Modeling of Spatially Varying Disease Rates (2005)

Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures...

Mixture Cure Survival Models with Dependent Censoring (2005)

Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures...

Mixture Cure Survival Models with Dependent Censoring (2005)

A number of authors have studies the mixture survival model to analyze survival data with nonnegligible cure fractions. A key assumption made by these authors is the independence between the survival...

Mixture Cure Survival Models with Dependent Censoring (2005)

A number of authors have studies the mixture survival model to analyze survival data with nonnegligible cure fractions. A key assumption made by these authors is the independence between the survival...

Mixture Cure Survival Models with Dependent Censoring (2005)

A number of authors have studies the mixture survival model to analyze survival data with nonnegligible cure fractions. A key assumption made by these authors is the independence between the survival...

Benchmark estimation for Markov Chain Monte Carlo samplers (2004)

A number of authors have studies the mixture survival model to analyze survival data with nonnegligible cure fractions. A key assumption made by these authors is the independence between the survival...

Benchmark estimation for Markov Chain Monte Carlo samplers

While studying various features of the posterior distribution of a vector-valued parameter using an MCMC sample, systematically subsampling of the MCMC output can only lead to poorer estimation....

Mixture cure survival models with dependent censoring

While studying various features of the posterior distribution of a vector-valued parameter using an MCMC sample, systematically subsampling of the MCMC output can only lead to poorer estimation....

Yi Li, Ram C. Tiwari, Subharup Guha

The paper is motivated by cure detection among the prostate cancer patients in the National Institutes of Health surveillance epidemiology and end results programme, wherein the main end point (e.g....

Generalized Poststratification and Importance Sampling for Subsampled Markov Chain Monte Carlo Estimation

Guha, Subharup, MacEachern, Steven N.

Bayesian Hidden Markov Modeling of Array CGH Data