104 citations · 136 across the 11 of their papers we have counts for
14 papers
On Tent Spaces for the Gaussian Measure
Liliana Forzani, Roberto Scotto, Wilfredo Urbina
Following the scheme of tent spaces in classical harmonic analysis developed by R. Coifman, Y. Meyer, and E. Stein in \cite{cms}, we succeed in doing so for the Gaussian setting. I…
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…
Sufficient reductions in regression with mixed predictors
Efstathia Bura, Liliana Forzani, Rodrigo García Arancibia +2
Most data sets comprise of measurements on continuous and categorical variables. In regression and classification Statistics literature, modeling high-dimensional mixed predictors…
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…
Asymptotic theory for maximum likelihood estimates in reduced-rank multivariate generalised linear models
Efstathia Bura, Sabrina Duarte, Liliana Forzani +2
Reduced-rank regression is a dimensionality reduction method with many applications. The asymptotic theory for reduced rank estimators of parameter matrices in multivariate linear…
On the classification problem for Poisson Point Processes
Alejandro Cholaquidis, Liliana Forzani, Pamela Llop +1
We study the binary classification problem for Poisson point processes, which are allowed to take values in a general metric space. The problem is tackled in two different ways: es…
Supervised dimension reduction for ordinal predictors
Liliana Forzani, Rodrigo García Arancibia, Pamela Llop +1
In applications involving ordinal predictors, common approaches to reduce dimensionality are either extensions of unsupervised techniques such as principal component analysis, or v…
Algorithms for Envelope Estimation II
Dennis Cook, Liliana Forzani, Zhihua Su
We propose a new algorithm for envelope estimation, along with a new root n consistent method for computing starting values. The new algorithm, which does not require optimization…
Estimating sufficient reductions of the predictors in abundant high-dimensional regressions
R. Dennis Cook, Liliana Forzani, Adam J. Rothman
We study the asymptotic behavior of a class of methods for sufficient dimension reduction in high-dimension regressions, as the sample size and number of predictors grow in various…
On convex regression estimators
Néstor E. Aguilera, Liliana Forzani, Pedro Morin
A new nonparametric estimator of a convex regression function in any dimension is proposed and its convergence properties are studied. We start by using any estimator of the regres…
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…
On the maximal function for the generalized Ornstein-Uhlenbeck semigroup
Jorge Betancor, Liliana Forzani, Roberto Scotto +1
In this note we consider the maximal function for the generalized Ornstein-Uhlenbeck semigroup in $\RR$ associated with the generalized Hermite polynomials and prove th…