Reduced-rank vector generalized linear models with two linear predictors
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- 作者：Thomas W. Yee
- 关键词：Generalized linear models ; Negative binomial regression ; Overdispersion ; Reduced-rank regression ; Variance function ; Zero-inflated Poisson
- 刊名：Computational Statistics and Data Analysis
- 出版年：March, 2014
- 年：2014
- 卷：71
- 期：Complete
- 页码：889-902
- 全文大小：702 K
文摘
Vector generalized linear models (VGLMs) as implemented in the vgam package permit multiple parameters to depend (via inverse link functions) on linear predictors. However it is often the case that one wishes different parameters to be related to each other in some way (i.e., to jointly satisfy certain constraints). Prominent and important examples of such cases include the normal or Gaussian family where one wishes to model the variance as a function of the mean, e.g., variance proportional to the mean raised to some power. Another example is the negative binomial family whose variance is approximately proportional to the mean raised to some power. It is shown that such constraints can be implemented in a straightforward manner via reduced rank regression (RRR) and easily used via the rrvglm() function. To this end RRR is briefly described and applied so as to impose parameter constraints in VGLMs with two parameters. The result is a rank-1 RR-VGLM. Numerous examples are given, some new, of the use of this technique. The implication here is that RRR offers hitherto undiscovered potential usefulness to many statistical distributions.