104 citations · 107 across the 7 of their papers we have counts for
5 papers · 1 filter
Sufficient dimension reduction for regression with spatially correlated errors: application to prediction
Liliana Forzani, Rodrigo García Arancibia, Antonella Gieco +2
In this paper, we address the problem of predicting a response variable in the context of both, spatially correlated and high-dimensional data. To reduce the dimensionality of the…
Asymptotic results for nonparametric regression estimators after sufficient dimension reduction estimation
Liliana Forzani, Daniela Rodriguez, Mariela Sued
Prediction, in regression and classification, is one of the main aims in modern data science. When the number of predictors is large, a common first step is to reduce the dimension…
Envelopes for multivariate linear regression with linearly constrained coefficients
Dennis Cook, Liliana Forzani, Lan Liu
A constrained multivariate linear model is a multivariate linear model with the columns of its coefficient matrix constrained to lie in a known subspace. This class of models inclu…
Fundamentals of path analysis in the social sciences
R. Dennis Cook, Liliana Forzani
Motivated by a recent series of diametrically opposed articles on the relative value of statistical methods for the analysis of path diagrams in the social sciences, we discuss fro…
Principal Fitted Components for Dimension Reduction in Regression
R. Dennis Cook, Liliana Forzani
We provide a remedy for two concerns that have dogged the use of principal components in regression: (i) principal components are computed from the predictors alone and do not make…