RT Journal Article
T1 An Approach to Canonical Correlation Analysis Based on Rényi’s Pseudodistances
A1 Jaenada Malagón, María
A1 Miranda Menéndez, Pedro
A1 Pardo Llorente, Leandro
A1 Zografos, Konstantinos
AB Canonical Correlation Analysis (CCA) infers a pairwise linear relationship between two groups of random variables, 𝑿 and 𝒀. In this paper, we present a new procedure based on Rényi’s pseudodistances (RP) aiming to detect linear and non-linear relationships between the two groups. RP canonical analysis (RPCCA) finds canonical coefficient vectors, 𝒂 and 𝒃, by maximizing an RP-based measure. This new family includes the Information Canonical Correlation Analysis (ICCA) as a particular case and extends the method for distances inherently robust against outliers. We provide estimating techniques for RPCCA and show the consistency of the proposed estimated canonical vectors. Further, a permutation test for determining the number of significant pairs of canonical variables is described. The robustness properties of the RPCCA are examined theoretically and empirically through a simulation study, concluding that the RPCCA presents a competitive alternative to ICCA with an added advantage in terms of robustness against outliers and data contamination.
PB MDPI
SN 1099-4300
YR 2023
FD 2023-04-25
LK https://hdl.handle.net/20.500.14352/87289
UL https://hdl.handle.net/20.500.14352/87289
LA eng
NO Ministerio de Ciencia e Innovación
DS Docta Complutense
RD 30 sept 2024