Abstract
This chapter looks at a subclass of VGLMs called Reduced-Rank VGLMs (RR-VGLMs). They are built on the idea of latent variables, and are the same as VGLMs except some of their constraint matrices are estimated. RR-VGLMs have with interesting properties and applications. It is a dimension-reduction method, e.g., when applied to the multinomial logit model it leads to the stereotype model. Another related class of models is row–column interaction models (RCIMs), and two-parameter RR-VGLMs are described in this chapter. Some applications mentioned here and/or developed elsewhere include quasi-variances and indirect gradient analysis.
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Yee, T.W. (2015). Reduced-Rank VGLMs. In: Vector Generalized Linear and Additive Models. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2818-7_5
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