Variable selection in generalized random coefficient autoregressive models

In this paper, we consider the variable selection problem of the generalized random coefficient autoregressive model (GRCA). Instead of parametric likelihood, we use non-parametric empirical likelihood in the information theoretic approach. We propose an empirical likelihood-based Akaike information...

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Detalles Bibliográficos
Autores principales: Zhao, Zhiwen, Liu, Yangping, Peng, Cuixin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5897497/
https://www.ncbi.nlm.nih.gov/pubmed/29674836
http://dx.doi.org/10.1186/s13660-018-1680-4
Descripción
Sumario:In this paper, we consider the variable selection problem of the generalized random coefficient autoregressive model (GRCA). Instead of parametric likelihood, we use non-parametric empirical likelihood in the information theoretic approach. We propose an empirical likelihood-based Akaike information criterion (AIC) and a Bayesian information criterion (BIC).

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