12 citations · 39 across the 9 of their papers we have counts for
10 papers
Fast Robust Regression via Orthogonal Block Updates
Anthony Christidis, Matias Salibian-Barrera
Robust regression methods, particularly MM-estimators, are essential for analyzing datasets where heavy-tailed noise or high-leverage outliers may be present. Algorithms to compute…
Robust nonparametric regression: review and practical considerations
Matias Salibian-Barrera
Nonparametric regression models offer a way to understand and quantify relationships between variables without having to identify an appropriate family of possible regression funct…
Tree-based boosting with functional data
Xiaomeng Ju, Matías Salibián-Barrera
In this article we propose a boosting algorithm for regression with functional explanatory variables and scalar responses. The algorithm uses decision trees constructed with multip…
Robust functional principal components for sparse longitudinal data
Graciela Boente, Matias Salibian-Barrera
In this paper we review existing methods for robust functional principal component analysis (FPCA) and propose a new method for FPCA that can be applied to longitudinal data where…
Modelling ocean temperatures from bio-probes under preferential sampling
Daniel Dinsdale, Matias Salibian-Barrera
In the last 25 years there has been an important increase in the amount of data collected from animal-mounted sensors (bio-probes), which are often used to study the animals' behav…
A robust and sparse K-means clustering algorithm
Yumi Kondo, Matias Salibian-Barrera, Ruben Zamar
In many situations where the interest lies in identifying clusters one might expect that not all available variables carry information about these groups. Furthermore, data quality…