Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications (Hardcover) (ISBN-13: 9781009410069)

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.

Author: Mikis D. Stasinopoulos, Thomas Kneib, Nadja Klein
ISBN-10: 1009410067
ISBN-13: 9781009410069
Publisher: Cambridge University Press
Language: English
Published: 02/29/2024
Pages: 306
Format: Hardcover
Weight: 1.65lbs
Size: 10.00h x 7.00w x 0.75d