Quantile-based graphical models for continuous and discrete variables
Year: 2023
Type: book-chapter
Abstract: In this paper we develop a mixed graphical model for identifying conditional independence \nrelations between continuous and discrete variables in a quantile framework using \nParzen’s definition of mid-quantile. To recover the graph structure and induce sparsity, we \nconsider the neighborhood selection approach in which conditional mid-quantiles of each \nvariable in the network are modeled as a sparse function of all others. Building on previous \nwork, we propose a two-step estimation procedure where, in the first step, conditional midprobabilities \nare obtained and, in the second step, the model parameters are estimated by \nsolving an implicit equation with a LASSO penalty. The empirical application investigates \nthe relationship between depression and inflammation on a sample of individuals from the \nNational Health and Nutrition Examination Survey 2017-2020.
Authors: Luca Merlo, Marco Geraci, Lea Petrella
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7023924449