A New Asymmetric Link-Based Binary Regression Model to Detect Parkinson's Disease by Using Replicated Voice Recordings
Naranjo Lizbeth, Carlos Javier Pérez, Jacinto Martín, Fernando Calle-Alonso · 2018
Addressing dependent data as independent has become usual for Parkinson's Disease (PD) detection by using features extracted from replicated voice recordings. A binary regression model with an Asymmetric Student t (AST) distribution as link function has been developed in a classification context by taking into account the within-subject dependence. This opens the possibility of handling situations in which the probabilities of the binary response approach 0 and 1 at different rates. The computational issue has been addressed by proposing and using a representation based on a mixture of normal distributions for the AST distribution. This allows to include latent variables to derive a Gibbs sampling algorithm that is used to generate samples from the posterior distribution. The applicability of the proposed approach has been tested with a simulation-based experiment and has been applied to a real dataset for PD detection.