Regularized Bayesian Estimation of Generalized Threshold Regression Models

Generalized Ridge Regression Estimator in Semiparametric Regression Models

In the context of ridge regression, the estimation of ridge (shrinkage) parameter plays an important role in analyzing data. Many efforts have been put to develop skills and methods of computing shrinkage estimators for different full-parametric ridge regression approaches, using eigenvalues. However, the estimation of shrinkage parameter is neglected for semiparametric regression models. The m...

Comparison of Maximum Likelihood Estimation and Bayesian with Generalized Gibbs Sampling for Ordinal Regression Analysis of Ovarian Hyperstimulation Syndrome

Background and Objectives: Analysis of ordinal data outcomes could lead to bias estimates and large variance in sparse one. The objective of this study is to compare parameter estimates of an ordinal regression model under maximum likelihood and Bayesian framework with generalized Gibbs sampling. The models were used to analyze ovarian hyperstimulation syndrome data.   Methods: This study use...

Bayesian Inference for Generalized Additive Regression based on Dynamic Models

We present a general approach for Bayesian inference via Markov chain Monte Carlo MCMC simulation in generalized additive semiparametric and mixed models It is particularly appropriate for discrete and other fundamentally non Gaussian responses where Gibbs sampling techniques developed for Gaussian models cannot be applied We use the close relation between nonparametric regression and dynamic o...

Adaptive Bayesian Regression Splines in Semiparametric Generalized Linear Models

This paper presents a fully Bayesian approach to regression splines with automatic knot selection in generalized semiparametric models for fundamentally non Gaussian responses In a basis function representation of the regression spline we use a B spline basis The reversible jump Markov chain Monte Carlo method allows for simultaneous estimation both of the number of knots and the knot placement...

Lecture 3: Bayesian estimation of spatial regression models