This book provides a broad overview of GAMLSS methodology and how it is implemented in R. It includes a comprehensive collection of real data examples, integrated code, and figures to illustrate the methods, and is supplemented by a website.
This book provides a broad overview of GAMLSS methodology and how it is implemented in R. It includes a comprehensive collection of real data examples, integrated code, and figures to illustrate the methods, and is supplemented by a website.
Mikis D. Stasinopoulos, Robert A. Rigby, Gillian Z. Heller, Vlasios Voudouris, Fernanda De Bastiani
Inhaltsangabe
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
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
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