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An emerging field in statistics, distributional regression facilitates the modelling of the complete conditional distribution, rather than just the mean. This book introduces generalized additive models for location, scale and shape (GAMLSS) – one of the most important classes of distributional regression. Taking a broad perspective, the authors consider penalized likelihood inference, Bayesian inference, and boosting as potential ways of estimating models and illustrate their usage in complex applications. Written by the international team who developed GAMLSS, the text's focus on practical questions and problems sets it apart. Case studies demonstrate how researchers in statistics and other data-rich disciplines can use the model in their work, exploring examples ranging from fetal ultrasounds to social media performance metrics. The R code and data sets for the case studies are available on the book's companion website, allowing for replication and further study.
'In a relatively short time, GAMLSS has become very popular. The driving force was the quality of the R package that made this powerful model easily accessible for applied statisticians. Despite the popularity of the model, the literature on GAMLSS is relatively small. This book fills a gap: it carefully presents the existing theory and adds extensions like Bayesian inference and boosting as well as new tools for interpreting GAMLSS models. In addition, it contains a large section with new and inspiring applications.'
Paul Eilers - Erasmus University Medical Center, Rotterdam, the Netherlands
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