Flexible Regression and Smoothing: Using Gamlss in R
Flexible Regression and Smoothing: Using Gamlss in R
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发表时间：
2017-05
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通讯作者：
M. Stasinopoulos;R. Rigby;G. Heller;V. Voudouris;F. Bastiani
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作者：
M. Stasinopoulos;R. Rigby;G. Heller;V. Voudouris;F. Bastiani
这本书是关于使用位置、比例和形状的广义加法模型(GAMLSS)从数据中学习。GAMLSS扩展了广义线性模型(GLMS)和广义加性模型(GAM)，以适应日益普遍的大型复杂数据集。特别是，GAMLSS统计框架使灵活的回归和平滑模型能够适应数据。GAMLSS模型假定响应变量具有任何参数分布(连续、离散或混合)，这些分布可能是重尾或轻尾的，也可能是正偏或负偏的。此外，分布的所有参数(位置、尺度、形状)都可以建模为解释变量的线性或光滑函数。
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.