A Bilinear Reduced Rank Model

Abstract

This article considers a bilinear model that includes two different latent effects. The first effect has a direct influence on the response variable, whereas the second latent effect is assumed to first influence other latent variables, which in turn affect the response variable. In this article, latent variables are modelled via rank restrictions on unknown mean parameters and the models which are used are often referred to as reduced rank regression models. This article presents a likelihoodbased approach that results in explicit estimators. In our model, the latent variables act as covariates that we know exist, but their direct influence is unknown and will therefore not be considered in detail. One example is if we observe hundreds of weather variables, but we cannot saywhich or howthese variables affect plant growth.

Published in

Title: Contemporary Experimental Design, Multivariate Analysis and Data Mining : Festschrift in Honour of Professor Kai-Tai Fang
Publisher: Springer International Publishing

SLU Authors

• von Rosen, Dietrich

  • Department of Energy and Technology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Probability Theory and Statistics

Publication identifier

• DOI: https://doi.org/10.1007/978-3-030-46161-4_21
• ISBN: 978-3-030-46160-7
• eISBN: 978-3-030-46161-4

Permanent link to this page (URI)

https://res.slu.se/id/publ/129845