A Bilinear Reduced Rank Model

Chengcheng Hao (), Feng Li () and Dietrich von Rosen ()
Additional contact information
Chengcheng Hao: Shanghai University of International Business and Economics
Feng Li: Central University of Finance and Economics
Dietrich von Rosen: Swedish University of Agricultural Sciences

Chapter Chapter 21 in Contemporary Experimental Design, Multivariate Analysis and Data Mining, 2020, pp 329-340 from Springer

Abstract: 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 likelihood-based 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 say which or how these variables affect plant growth.

Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-46161-4_21

Ordering information: This item can be ordered from
http://www.springer.com/9783030461614

DOI: 10.1007/978-3-030-46161-4_21

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().