Meet the Exponential Family
Meet the Exponential Family
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认识指数家族
DOI：
10.1007/978-0-387-87458-6_8
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Graham M. Smith
中科院分区：
文献类型：
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作者：
A. Zuur;E. Ieno;N. Walker;A. Saveliev;Graham M. Smith
在第 2 章和第 3 章以及附录 A 中，讨论了线性回归和加性建模，并在第 4、5、6 和 7 章中讨论了允许不同方差、嵌套数据、时间相关性和空间相关性的各种扩展。在第 8、9 和 10 章中，我们讨论了广义线性建模 (GLM) 和广义加性建模 (GAM) 技术。在线性回归和加性建模中，我们使用正态（或：高斯）分布。重要的是要认识到该分布适用于响应变量。 GLM 和 GAM 是线性和加性建模的扩展，因为使用响应变量的非高斯分布，并且响应变量和解释变量之间的关系（或链接）可能不同。本章我们关注第一点，分布。
In Chapters 2 and 3 and in Appendix A, linear regression and additive modelling were discussed and various extensions allowing for different variances, nested data, temporal correlation, and spatial correlation were then discussed in Chapters 4, 5, 6, and 7. In Chapters 8, 9, and 10, we discuss generalised linear modelling (GLM) and generalised additive modelling (GAM) techniques. In linear regression and additive modelling, we use the Normal (or: Gaussian) distribution. It is important to realise that this distribution applies for the response variable. GLM and GAM are extensions of linear and additive modelling in the sense that a non-Gaussian distribution for the response variable is used and the relationship (or link) between the response variable and the explanatory variables may be different. In this chapter, we focus on the first point, the distribution.