Robust fitting for generalized additive models for location, scale and shape

Bayesian generalized additive models for location, scale and shape for zero-inflated and overdispersed count data

Frequent problems in applied research that prevent the application of the classical Poisson log-linear model for analyzing count data include overdispersion, an excess of zeros compared to the Poisson distribution, correlated responses, as well as complex predictor structures comprising nonlinear effects of continuous covariates, interactions or spatial effects. We propose a general class of Ba...

A distributed algorithm for fitting generalized additive models

Generalized additive models are an effective regression tool, popular in the statistics literature, that provides an automatic extension of traditional linear models to nonlinear systems. We present a distributed algorithm for fitting generalized additive models, based on the alternating direction method of multipliers (ADMM). In our algorithm the component functions of the model are fit indepe...

BAMLSS: Bayesian additive models for location, scale and shape (and beyond)

Bayesian analysis provides a convenient setting for the estimation of complex generalized additive regression models (GAMs). Since computational power has tremendously increased in the past decade it is now possible to tackle complicated inferential problems, e.g., with Markov chain Monte Carlo simulation, on virtually any modern computer. This is one of the reasons why Bayesian methods have be...

Nonlife ratemaking and risk management with bayesian additive models for location, scale and shape

Generalized additive models for location, scale and shape define a flexible, semiparametric class of regression models for analyzing insurance data in which the exponential family assumption for the response is relaxed. This approach allows the actuary to include risk factors not only in the mean but also in other parameters governing the claiming behavior, like the degree of residual heterogen...

AMlet and GAMlet: Automatic Nonlinear Fitting of Additive Models and Generalized Additive Models with Wavelets