𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Selecting both latent and explanatory variables in the PLS1 regression model

✍ Scribed by Aziz Lazraq; Robert Cléroux; Jean-Pierre Gauchi

Publisher
Elsevier Science
Year
2003
Tongue
English
Weight
200 KB
Volume
66
Category
Article
ISSN
0169-7439

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Regression by L1 regularization of smart
Regression by L1 regularization of smart contrasts and sums (ROSCAS) beats PLS and elastic net in latent variable model
✍ Cajo J. F. ter Braak 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 559 KB

## Abstract This paper proposes a regression method, ROSCAS, which regularizes smart contrasts and sums of regression coefficients by an __L__~1~ penalty. The contrasts and sums are based on the sample correlation matrix of the predictors and are suggested by a latent variable regression model. The

The extended switching regression model:
The extended switching regression model: allowing for multiple latent state variables
✍ Arie Preminger; Uri Ben-zion; David Wettstein 📂 Article 📅 2007 🏛 John Wiley and Sons 🌐 English ⚖ 286 KB

## Abstract In this paper we extend the widely followed approach of switching regression models, i.e. models in which the parameters are determined by a latent discrete state variable. We construct a model with several latent state variables, where the model parameters are partitioned into disjoint

Bootstrap-based Q̂kh2 for the selection
Bootstrap-based Q̂kh2 for the selection of components and variables in PLS regression
✍ Silvano Amato; Vincenzo Esposito Vinzi 📂 Article 📅 2003 🏛 Elsevier Science 🌐 English ⚖ 249 KB

The aim of this paper is to suggest a bootstrap-based method for choosing the number of components in Partial Least Squares Regression (PLSR). Cross-validated Q h 2 statistic is used, for which is intended to derive a bootstrap distribution and to perform a hypothesis testing. Monte Carlo approximat