1st Edition
Flexible Regression and Smoothing Using GAMLSS in R
Part I Introduction to models and packages
Why GAMLSS?
Introduction to the gamlss packages
Part II The R implementation: algorithms and functions
The Algorithms
The gamlss() function
Methods for fitted gamlss objects
Part III Distributions
The gamlss.family of distributions
Finite mixture distributions
Part IV Additive terms
Linear parametric additive terms
Additive Smoothing Terms
Random effects
Part V Model selection and diagnostics
Model selection techniques
Diagnostics
Part VI Applications
Centile Estimation
Further Applications
Biography
Mikis D. Stasinopoulos, Robert A. Rigby, Gillian Z. Heller, Vlasios Voudouris, Fernanda De Bastiani
"That the authors succeed in communicating the process of learning from data using the GAMLSS suite of tool is due to the clear and effective organization of the book. The book is a complete introduction to GAMLSS models (and by extension GLMs and GAMs) as well as some newer techniques such as semi-parametric neural networks/deep learning and trees. I highly recommend it to any reader interested in advanced machine learning techniques."
—Carlo Di Maio, European Central Bank"’Flexible Regression and Smoothing: Using GAMLSS in R’ is a comprehensive and authoritative text from the co-authors of perhaps the most flexible regression modeling framework in statistics and supervised machine learning. Traditional regression approaches focus on the mean of the distribution conditional on a set of predictor variables. GAMLSS extends this up to four distribution parameters which are modeled as additive functions of predictor variables. Through this extension, the analyst has a choice of over 90 continuous, discrete and mixed distributions for the response variable which allows modeling of highly skewed and kurtotic distributions while improving transparency and interpretability for the effects of predictor variables driving the model. This well-written book details the methodology and R packages underlying the framework including algorithms, model fitting, additive terms, model diagnostics and examples with real data. The impact of GAMLSS has been demonstrated in many industries including medicine, environmental science, biology, finance and insurance. Data scientists, quantitative analysts and researchers will be enlightened when discovering the myriad of modeling opportunities through the material in this landmark text."
—Edward Tong, PhD"Generalized additive models for location, scale, and shape (GAMLSS) as introduced by Bob Rigby and Mikis Stasinopoulos in their seminal 2005 paper are one versatile, yet simple me
