PLS regression on a stochastic process

PLS regression on a stochastic process

Partial least squares (PLS) regression on an L2-continuous stochastic process is an extension of the 2nite set case of predictor variables. The PLS components existence as eigenvectors of some operator and convergence properties of the PLS approximation are proved. The results of an application to stock-exchange data will be compared with those obtained by other methods. c © 2003 Elsevier B.V. ...

Clusterwise PLS regression on a stochastic process

In this paper we propose to use the PLS approach for clusterwise linear regression in the particular case where the set of predictor variables forms a L2-continuous stochastic process {Xt}t∈[0,T ]. We have adapted the k-means algorithm to this case and we give necessar conditions for its convergence. The results of an application of the clusterwise PLS regression to stock-exchange data are comp...

Globally sparse PLS regression

Partial least squares (PLS) regression combines dimensionality reduction and prediction using a latent variable model. It provides better predictive ability than principle component analysis by taking into account both the independent and response variables in the dimension reduction procedure. However, PLS suffers from over-fitting problems for few samples but many variables. We formulate a ne...

PLS generalised linear regression

PLS univariate regression is a model linking a dependent variable y to a set X= {x1; : : : ; xp} of (numerical or categorical) explanatory variables. It can be obtained as a series of simple and multiple regressions. By taking advantage from the statistical tests associated with linear regression, it is feasible to select the signi6cant explanatory variables to include in PLS regression and to ...

Robust PLS regression based on simple least median squares regression