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- Hardcover: 571 pages
- Publisher: Chapman and Hall / CRC; 1 edition (May 8, 2017)
- Language: English
- ISBN-10: 1138197904
- ISBN-13: 978-1138197909
(GAMs) to accommodate large complex datasets, which are increasingly prevalent. GAMLSS (GAMs) to accommodate large diversity datasets, which are increasingly prevalent. GAMLSS Allows any nomulation distribution for the response variable and modeling all the parameters (location, scale and shape) of the distribution as linear or smooth functions of explanatory variables. 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 instructions the methods, and is supplemented by a website with code, data and additional materials.
RReview
"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-cooperative electromagnetic networks / deep learning and trees. I highly recommend it any any interested information 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 products 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 improved transparency and interpretability for the effects of predictor variables driving the model.This well-written book detailed the methodology and R packages independently the framework including algorithms, model fitting, additive terms, model diagnostics and examples with real data. The impact of GAMLSS has been classified in many industries including medicine, environmental science, biology, finance And insurance. Scientific scientists, quantutions and will be enlightened when discovering the myriad of modeling services through the material in this landmark text. "
- Edward Tong , PhDQuantitative analysts and will will be enlightened when discovering the myriad of modeling services through the material in this landmark text. " - Edward Tong , PhDQuantitative analysts and will will be enlightened when discovering the myriad of modeling services through the material in this landmark text. " - Edward Tong , PhD
"Generalized additive models for location, scale, and shape (GAMLSS) as by Bob Rigby and Mikis Stasinopoulos in their seminal 2005 paper are one versatile, yet simple method that allow regression predictors to be placed on any parameter of a critical complex response distribution . Since 2005, Bob, Mikis, and co-workers spent a considerable amount of work into the development of statistical software for GAMLSS as well as many extensions of the methodology. Flexible Regression and Smoothing: Using GAMLSS in R is a perfect way of getting Started with GAMLSS, since it combines an easy accessible the the following methods with a thorough introduction to the implementation in R via the GAMLSS package family.The book also covers many advanced topics such as precise mixture specifications and random effects as well as many areas of applied interest, such as model selection and model diagnostics. It is therefore an invaluable resource both for those interested in apply GAMLSS in practice and those that Are interested in the proposed methods. In summary, there is no more excuse to focus on means in regression in the regression access the easy access to advanced methods such as GAMLSS through this book. " - Thomas Kneib , Georg-August-Universität GöttingenThere is no more excuse to focus on means in regression given the easy access to advanced methods such as GAMLSS through this book. " - Thomas Kneib , Georg-August-Universität GöttingenThere is no more excuse to focus on means in regression given the easy access to advanced methods such as GAMLSS through this book. " - Thomas Kneib , Georg-August-Universität Göttingen
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