Direct generalized additive modeling with penalized likelihood
Computational Statistics & Data Analysis
Computer Solution of Large Sparse Positive Definite
Computer Solution of Large Sparse Positive Definite
Sampling from the posterior distribution in generalized linear mixed models
Statistics and Computing
Space-varying regression models: specifications and simulation
Computational Statistics & Data Analysis - Special issue: Computational econometrics
Choice of B-splines with free parameters in the flexible discriminant analysis context
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
3D space-varying coefficient models with application to diffusion tensor imaging
Computational Statistics & Data Analysis
Archimedean copula estimation using Bayesian splines smoothing techniques
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Parametrization and penalties in spline models with an application to survival analysis
Computational Statistics & Data Analysis
Bayesian density estimation from grouped continuous data
Computational Statistics & Data Analysis
Additive models in censored regression
Computational Statistics & Data Analysis
Locally adaptive Bayesian P-splines with a Normal-Exponential-Gamma prior
Computational Statistics & Data Analysis
Bayesian analysis of semiparametric reproductive dispersion mixed-effects models
Computational Statistics & Data Analysis
Bayesian inference for additive mixed quantile regression models
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Semiparametric regression with shape-constrained penalized splines
Computational Statistics & Data Analysis
Bayesian model selection for logistic regression models with random intercept
Computational Statistics & Data Analysis
Nonparametric additive location-scale models for interval censored data
Statistics and Computing
Multilevel structured additive regression
Statistics and Computing
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Generalized additive models (GAM) for modeling nonlinear effects of continuous covariates are now well established tools for the applied statistician. A Bayesian version of GAM's and extensions to generalized structured additive regression (STAR) are developed. One or two dimensional P-splines are used as the main building block. Inference relies on Markov chain Monte Carlo (MCMC) simulation techniques, and is either based on iteratively weighted least squares (IWLS) proposals or on latent utility representations of (multi)categorical regression models. The approach covers the most common univariate response distributions, e.g., the binomial, Poisson or gamma distribution, as well as multicategorical responses. For the first time, Bayesian semiparametric inference for the widely used multinomial logit model is presented. Two applications on the forest health status of trees and a space-time analysis of health insurance data demonstrate the potential of the approach for realistic modeling of complex problems. Software for the methodology is provided within the public domain package BayesX.