The place of probability distributions in statistical learning. A commented book review of ‘Distributions for modeling location, scale, and shape using GAMLSS in R’ by Rigby et al. (2021)

AbstractGeneralised additive models for location, scale and shape (GAMLSS) is a type of distributional regression framework that enables modelling numeric dependent variables via probability distributions other than those of the exponential family. While the cogs behind GAMLSS are provided in Stasinopouloset al. 2017's book ‘Flexible regression and smoothing using GAMLSS in R, the new book by Rigbyet al. considers the distributions implemented in the R software that are usable for GAMLSS modelling. A commented summary of that second book is provided in a supplementary file. Unlike traditional book reviews, two topics in this new book are briefly elaborated on: robustness (Chapter 12) and shape (Chapters 14 –16). It is concluded that despite GAMLSS being a powerful and flexible framework for supervised statistical learning, striving for interpretable GAMLSS models is essential.

Source: Australian and New Zealand Journal of Statistics - September 23, 2022 Category: Statistics Authors: Fernando Marmolejo ‐Ramos, Raydonal Ospina, Freddy Hernández‐Barajas Tags: Original Article Source Type: research