𝔖 Bobbio Scriptorium
✦   LIBER   ✦

A computational framework for variable selection in multivariate regression

✍ 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.


πŸ“œ SIMILAR VOLUMES


Frameworks for latent variable multivari
✍ Alison J. Burnham; Roman Viveros; John F. MacGregor πŸ“‚ Article πŸ“… 1996 πŸ› John Wiley and Sons 🌐 English βš– 909 KB

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 statistical framework for multivariate
✍ Alison J. Burnham; John F. MacGregor; Roman Viveros πŸ“‚ Article πŸ“… 1999 πŸ› John Wiley and Sons 🌐 English βš– 124 KB πŸ‘ 1 views

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

Variable selection for neural networks i
✍ FrΓ©dΓ©ric Despagne; DΓ©sirΓ©-Luc Massart πŸ“‚ Article πŸ“… 1998 πŸ› Elsevier Science 🌐 English βš– 397 KB

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

Bayesian variable selection for survival
✍ Ioanna Tachmazidou; Michael R. Johnson; Maria De Iorio πŸ“‚ Article πŸ“… 2010 πŸ› John Wiley and Sons 🌐 English βš– 224 KB

## 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 Bayesian‐inspired penalized maximum likelihood approach appropriate for high‐di