Generalized linear models
Generalized linear models
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DOI：
10.1002/wics.175
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发表时间：
2011-08
期刊：
影响因子：
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通讯作者：
John Neuhaus;Charles McCulloch
中科院分区：
文献类型：
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作者：
John Neuhaus;Charles McCulloch
一类广义线性模型（GLMs）扩展了经典的连续、正态响应线性模型，以描述一个或多个预测变量x1、…、xp与各种非正态分布响应Y（包括二进制、计数和正值变量）之间的关系。glm将响应密度的类别从正态扩展到指数族，其中包含正态分布，泊松分布，二项分布和其他常见分布作为特殊情况。模型产生符合响应约束的估计期望值，并允许预测者和期望值之间的非线性关系。构造一组数据的似然是很简单的，因此最大似然和相关的基于似然的方法是参数估计和推断的流行技术。glm的一个关键点是，模型构建中的许多考虑因素与标准线性回归模型相同，因为模型具有许多共同特征。WIREs Comp Stat 2011 3 407-413 DOI: 10.1002/wics.175
The class of generalized linear models (GLMs) extends the classical linear model for continuous, normal responses to describe the relationship between one or more predictor variables x1,…,xp and a wide variety of nonnormally distributed responses Y including binary, count, and positive‐valued variates. GLMs expand the class of response densities from the normal to an exponential family that contains the normal, Poisson, binomial, and other popular distributions as special cases. The models produce estimated expected values that conform to response constraints and allow nonlinear relationships between predictors and expected values. It is straightforward to construct the likelihood for a set of data so that maximum likelihood and related likelihood‐based methods are popular techniques for parameter estimation and inference. A key point with GLMs is that many of the considerations in model construction are the same as for standard linear regression models as the models have many common features. WIREs Comp Stat 2011 3 407–413 DOI: 10.1002/wics.175