Partial least square (PLS) methods (also sometimes called projection to latent structures) 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 ap...

A weighted linear regression model with impercise response and p-real explanatory variables is analyzed. The LR fuzzy random variable is introduced and a metric is suggested for coping with this kind of variables. A least square solution for estimating the parameters of the model is derived. The result are illustrated by the means of some case studies.

-We study the least square regression with data dependent hypothesis and coefficient regularization algorithms based on general kernel. An explicit expression of the solution of this kernel scheme is derived. Then we provide a sample error with a decay of 1 ( ) O m and estimate the approximation error in terms of some kind of K -functional. Index Terms -Least Square Regressions, Data Dependent ...

The diffusion least mean square (DLMS) and the normalized (DNLMS) algorithms are analyzed for a network having fusion center. This structure reduces dimensionality of resulting stochastic models while preserving important properties. analysis is done in system identification framework cyclostationary white nodal inputs. parameters vary according to random walk model. cyclostationarity modeled b...