Opened Access Modelo de regresión PLS
Cites
Show item statistics
Icon
Export to
Author: Márquez Ruiz, Cristina
Director: Pino Mejías, Rafael
Department: Universidad de Sevilla. Departamento de Estadística e Investigación Operativa
Date: 2017-06
Document type: Final Degree Work
Academic Title: Universidad de Sevilla. Grado en Estadística
Abstract: Partial least Square (PLS) methods relate the information present in two data tables that collect measurements on the same set of observations. PLS methods proceed by deriving latent variables which are (optimal) linear combinations of the variables of a data table. When the goal is to find the shared information between two tables, the approach is equivalent to a correlation problem and the technique is then called Partial Least Square correlation. In this case there are two sets of latent variables (one set per table), and these latent variables are required to have maximal covariance. When the goal is to predict one data table the other one, the technique is then called Partial Least Square regression. In this case there is one set of latent variables (derived from the predictor table) and these latent variables are required to give the best posible prediction.
Size: 1.779Mb
Format: PDF

URI: http://hdl.handle.net/11441/63208

This work is under a Creative Commons License: 
Attribution-NonCommercial-NoDerivatives 4.0 Internacional

This item appears in the following Collection(s)