 Generalized Linear ModelsDirk P. Kroese and
Joshua Chan
Chapter Chapter 9 in Statistical Modeling and Computation, 2014, pp 265-286 from Springer Abstract: Abstract The linear models introduced in Chap. 4 deal with continuous response variables—such as height and crop yield—and continuous or discrete explanatory variables. For example, under a normal linear model, the responses $$\{Y _{i}\}$$ are independent of each other, and each has a normal distribution with mean $$\mu _{i} = \mathbf{x}_{i}^{\top }\boldsymbol{\beta }$$ , where $$\mathbf{x}_{i}^{\top }$$ is the ith row of the design matrix X. However, these continuous models are obviously not suitable for data that take on discrete values. For example, we might want to analyze women’s labor market participation decision (whether to work or not), voters’ opinion of the government (rating on the government performance on a scale of five), or the choice among a few cereal brands, as a function of one or more explanatory variables. In this chapter we discuss models that are suitable for analyzing these discrete response variables. We will first introduce the flexible framework of generalized linear models. Keywords: Score Function; Probit Model; Information Matrix; Data Augmentation; Observe Information Matrix (search for similar items in EconPapers) There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4614-8775-3_9 Ordering information: This item can be ordered from DOI: 10.1007/978-1-4614-8775-3_9 Access Statistics for this chapter More chapters in Springer Books from Springer |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.