104 citations · 136 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…