Transformation and weighting in regression
Transformation and weighting in regression
Multivariate reduced rank regression in non-Gaussian contexts, using copulas
Computational Statistics & Data Analysis
Quasi-negative binomial distribution: Properties and applications
Computational Statistics & Data Analysis
Robust joint modeling of mean and dispersion through trimming
Computational Statistics & Data Analysis
Editorial: Special issue on statistical algorithms and software in R
Computational Statistics & Data Analysis
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Vector generalized linear models (VGLMs) as implemented in the vgamR 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.