2 citations · 2 across the 3 of their papers we have counts for
4 papers
Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure
María Florencia Acosta, Rodrigo García Arancibia, Pamela Llop +2
This paper presents a novel value-aware approach to product recommendation that simultaneously addresses the high dimensionality and sparsity of user-item data while explicitly inc…
A semiparametric autorregresive spatial prediction model
Rodrigo García Arancibia, Pamela Llop, Mariel Lovatto
In this paper we propose a semiparametric spatial autoregressive model that combines a linear covariate component with a nonparametrically estimated spatial term, allowing flexible…
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…
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…