真线性模型
贝叶斯多元线性回归
线性预测函数
一般线性模型
线性回归
回归诊断
线性模型
回归分析
多元自适应回归样条
分段回归
广义线性模型
因子回归模型
数学
统计
主成分回归
多项式回归
多级模型
出处
期刊:Elsevier eBooks
[Elsevier]
日期:2023-01-01
卷期号:: 548-557
被引量:15
标识
DOI:10.1016/b978-0-12-818630-5.10067-3
摘要
Linear regression is one of the most broadly applied statistical techniques to investigate the linear relationship between one single dependent variable and one or more than one independent variable. It is also considered the foundation of statistical learning theory. In this entry, we illustrate the standard linear regression model, review regression coefficient estimation approaches, and discuss major assumptions underlying the linear regression model. In addition, we present three extensions of the linear regression model, including the generalized linear model (GLM), the hierarchical linear model (HLM), and multivariate regression model.
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