Variable selection in generalized random coefficient autoregressive models
Abstract 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 in...
Main Authors: | Zhiwen Zhao, Yangping Liu, Cuixin Peng |
---|---|
Format: | Article |
Language: | English |
Published: |
SpringerOpen
2018-04-01
|
Series: | Journal of Inequalities and Applications |
Subjects: | |
Online Access: | http://link.springer.com/article/10.1186/s13660-018-1680-4 |
Similar Items
-
Improved Treatment of the Independent Variables for the Deployment of Model Selection Criteria in the Analysis of Complex Systems
by: Luca Spolladore, et al.
Published: (2021-09-01) -
Estimation of Asymmetric Spatial Autoregressive Dependence on Irregular Lattices
by: Franz H. Harke, et al.
Published: (2022-07-01) -
Modeling and Forecasting the Third wave of Covid-19 Incidence Rate in Nigeria Using Vector Autoregressive Model Approach
by: Gabriel O. Odekina, et al.
Published: (2022-02-01) -
Model selection in multivariate adaptive regressions splines (MARS) using alternative information criteria
by: Meryem Bekar Adiguzel, et al.
Published: (2023-09-01) -
Deriving Proper Uniform Priors for Regression Coefficients, Parts I, II, and III
by: H.R. Noel van Erp, et al.
Published: (2017-05-01)