Boosting Multivariate Structured Additive Distributional Regression Models

07/18/2022
by   Annika Strömer, et al.
0

We develop a model-based boosting approach for multivariate distributional regression within the framework of generalized additive models for location, scale, and shape. Our approach enables the simultaneous modeling of all distribution parameters of an arbitrary parametric distribution of a multivariate response conditional on explanatory variables, while being applicable to potentially high-dimensional data. Moreover, the boosting algorithm incorporates data-driven variable selection, taking various different types of effects into account. As a special merit of our approach, it allows for modelling the association between multiple continuous or discrete outcomes through the relevant covariates. After a detailed simulation study investigating estimation and prediction performance, we demonstrate the full flexibility of our approach in three diverse biomedical applications. The first is based on high-dimensional genomic cohort data from the UK Biobank, considering a bivariate binary response (chronic ischemic heart disease and high cholesterol). Here, we are able to identify genetic variants that are informative for the association between cholesterol and heart disease. The second application considers the demand for health care in Australia with the number of consultations and the number of prescribed medications as a bivariate count response. The third application analyses two dimensions of childhood undernutrition in Nigeria as a bivariate response and we find that the correlation between the two undernutrition scores is considerably different depending on the child's age and the region the child lives in.

READ FULL TEXT

page 9

page 27

page 42

research
02/25/2022

Boosting Distributional Copula Regression

Capturing complex dependence structures between outcome variables (e.g.,...
research
06/05/2023

Truly Multivariate Structured Additive Distributional Regression

Generalized additive models for location, scale and shape (GAMLSS) are a...
research
11/01/2017

Bayesian Variable Selection for Multivariate Zero-Inflated Models: Application to Microbiome Count Data

Microorganisms play critical roles in human health and disease. It is we...
research
08/09/2022

Copulaboost: additive modeling with copula-based model components

We propose a type of generalised additive models with of model component...
research
01/13/2023

Scalable Estimation for Structured Additive Distributional Regression

Recently, fitting probabilistic models have gained importance in many ar...
research
04/02/2022

Distributional Gradient Boosting Machines

We present a unified probabilistic gradient boosting framework for regre...
research
02/18/2021

Adaptive Step-Length Selection in Gradient Boosting for Generalized Additive Models for Location, Scale and Shape

Tuning of model-based boosting algorithms relies mainly on the number of...

Please sign up or login with your details

Forgot password? Click here to reset