Universidad Complutense de Madrid
E-Prints Complutense

Model Selection in a Composite Likelihood Framework Based on Density Power Divergence

Downloads

Downloads per month over past year

63188

Impacto

Downloads

Downloads per month over past year

Castilla González, Elena and Martín Apaolaza, Nirian and Pardo Llorente, Leandro and Zografos, Konstantinos (2020) Model Selection in a Composite Likelihood Framework Based on Density Power Divergence. Entropy, 22 (3). p. 270. ISSN 1099-4300

[thumbnail of entropy-22-00270-v3.pdf]
Preview
PDF
Creative Commons Attribution.

360kB

Official URL: https://doi.org/10.3390/e22030270




Abstract

This paper presents a model selection criterion in a composite likelihood framework based on density power divergence measures and in the composite minimum density power divergence estimators, which depends on an tuning parameter α. After introducing such a criterion, some asymptotic properties are established. We present a simulation study and two numerical examples in order to point out the robustness properties of the introduced model selection criterion.


Item Type:Article
Uncontrolled Keywords:Composite likelihood; Composite minimum density power divergence estimators; Model selection
Palabras clave (otros idiomas):Probabilidad compuesta; Probabilidades
Subjects:Sciences > Mathematics
Sciences > Mathematics > Probabilities
ID Code:63188
Deposited On:30 Nov 2020 12:46
Last Modified:30 Nov 2020 15:43

Origin of downloads

Repository Staff Only: item control page