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Flexible Regression and Smoothing - Using GAMLSS in R (Hardcover)
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Flexible Regression and Smoothing - Using GAMLSS in R (Hardcover)
Series: Chapman & Hall/CRC The R Series
Expected to ship within 12 - 17 working days
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This book is about learning from data using the Generalized
Additive Models for Location, Scale and Shape (GAMLSS). GAMLSS
extends the Generalized Linear Models (GLMs) and Generalized
Additive Models (GAMs) to accommodate large complex datasets, which
are increasingly prevalent. In particular, the GAMLSS statistical
framework enables flexible regression and smoothing models to be
fitted to the data. The GAMLSS model assumes that the response
variable has any parametric (continuous, discrete or mixed)
distribution which might be heavy- or light-tailed, and positively
or negatively skewed. In addition, all the parameters of the
distribution (location, scale, shape) can be modelled as linear or
smooth functions of explanatory variables. Key Features: Provides a
broad overview of flexible regression and smoothing techniques to
learn from data whilst also focusing on the practical application
of methodology using GAMLSS software in R. Includes a comprehensive
collection of real data examples, which reflect the range of
problems addressed by GAMLSS models and provide a practical
illustration of the process of using flexible GAMLSS models for
statistical learning. R code integrated into the text for ease of
understanding and replication. Supplemented by a website with code,
data and extra materials. This book aims to help readers understand
how to learn from data encountered in many fields. It will be
useful for practitioners and researchers who wish to understand and
use the GAMLSS models to learn from data and also for students who
wish to learn GAMLSS through practical examples.
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