J. Fernández Serrano, J. E. Chacón
The number of modes in a probability density function is representative of the model’s complexity and can also be viewed as the number of existing subpopulations. Despite its relevance, little research has been devoted to its estimation. Focusing on the univariate setting, we propose a novel approach that builds upon a combination of flexible kernel estimators and parsimonious compositional splines. Feature exploration, model selection and mode testing are implemented in the Bayesian inference paradigm, providing soft solutions and allowing to incorporate expert judgement in the process. The usefulness of our proposal is illustrated through a case study in sports analytics and a thorough simulation study. In this context, our method emerges as a top-tier alternative offering innovative solutions for analysts.
Keywords: number of modes, Bayesian inference, compositional spline, kernel density estimation, model selection, mode testing
Scheduled
GT09.NOPAR2 Invited session Mode analysis
November 8, 2023 4:00 PM
HC2: Canónigos Room 2