A set of frameworks for latent variable multivariate regression method is developed. The first two of these frameworks describe the objective functions satisfied by the latent variables chosen in canonical coordinates regression (CCR), reduced rank regression (RRR) and SIMPLS. These frameworks show
A computational framework for variable selection in multivariate regression
β Scribed by Bruce E. Barrett; J. Brian Gray
- Publisher
- Springer US
- Year
- 1994
- Tongue
- English
- Weight
- 856 KB
- Volume
- 4
- Category
- Article
- ISSN
- 0960-3174
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
A statistical framework is developed to contrast methods used for parameter estimation for a latent variable multivariate regression (LVMR) model. This model involves two sets of variables, X and Y, both with multiple variables and sharing a common latent structure with additive random errors. The m
The problem of variable selection for neural network modeling is discussed in this paper. Two methods that gave the best results in a previous comparative study are presented. One of these methods is a modified version of the Hinton diagrams, the other method is based on saliency estimation and is p
## Abstract Variable selection in regression with very big numbers of variables is challenging both in terms of model specification and computation. We focus on genetic studies in the field of survival, and we present a Bayesianβinspired penalized maximum likelihood approach appropriate for highβdi