Repeated measures in functional logistic regression
Metadatos
Mostrar el registro completo del ítemEditorial
Elsevier
Materia
Functional data Functional logistic regression Random effects
Fecha
2024-05-10Referencia bibliográfica
Urbano Leon, C.L. & Aguilera, A.M. & Escabias, M. 225 (2024) 66–77. [https://doi.org/10.1016/j.matcom.2024.05.002]
Patrocinador
PID2020-113961GB-I00 project of the Spanish Ministry of Science and Innovation (also supported by the FEDER program); FQM-307 of the Autonomous Government of Andalusia (Spain); IMAG Maria de Maeztu grant CEX2020-001105-M/AEI/10.13039/501100011033Resumen
We present a proposal to extend the functional logistic regression model – which models a
binary scalar response variable from a functional predictor – to the case where the functional
observations are not independent because the same functional variable is measured in the same
individuals in different experimental conditions (repeated measures). The extension is addressed
by including a random effect in the model. The functional approach of this model assumes
that all functional objects are elements of the same finite-dimensional subspace of the space
of square-integrable functions 𝐿������2 in the same compact domain allowing the functions to be
treated through the basis coefficients on the basis that spans the subspace to which functional
objects belong (basis expansion). This methodology usually induces a multicollinearity problem
in the multivariate model that emerges, which is solved with the use of the functional principal
components of the functional predictor, resulting in a new functional principal component
random effects model. The proposal is contextualized through a simulation study that contains
three simulation scenarios for four different functional parameters and considering the lack of
independence.