This course introduces the generalized linear models and
their applications to a variety of types of data.

This course introduces the generalized linear models and
their applications to a variety of types of data.

廣義線性模型是一門應用極廣且兼具理論架構的統計模式。本門課擬就廣義線性模式之統計推論做一介紹，並就應用廣義線性模式於不同的資料形態來做方法之介紹以及實際軟體編寫。其中包含二項式資料之邏輯斯迴歸模式，分析多項式資料之proportional-odds models，分析卜松分布型資料的log-linear models 以及分析存活資料之相關方法及模式，除以之外，一些基礎的估計理論，包含conditional likelihood function以及quasi-likelihood function亦是涵蓋之主題。

Generalized linear model is a statistical model with extremely wide application and theoretical framework. This course is intended to introduce the statistical inference of generalized linear models, and introduce methods and practical software programming for applying generalized linear models to different data forms. It includes logistic regression models for binomial data, proportional-odds models for analyzing polynomial data, log-linear models for analyzing Buson distribution data, and related methods and models for analyzing survival data. In addition, some basic estimation theories, including conditional likelihood function and quasi-likelihood function, are also covered topics.

Generalized Linear Models (2nd edition) ,
by Nelder and McCullagh.

Generalized Linear Models (2nd edition) ,
by Nelder and McCullagh.