Adaptive lasso for linear regression models with ARMA-GARCH errors

Autor:	Sooyong Lee, Taewook Lee, Young Joo Yoon
Rok vydání:	2015
Předmět:	Statistics and Probability Lasso (statistics) Modeling and Simulation Autoregressive conditional heteroskedasticity Linear regression Statistics Autocorrelation Errors-in-variables models Feature selection Autoregressive–moving-average model Focus (optics) Mathematics
Zdroj:	Communications in Statistics - Simulation and Computation. :1-12
ISSN:	1532-4141 0361-0918
DOI:	10.1080/03610918.2015.1096372
Popis:	The linear regression models with the autoregressive moving average (ARMA) errors (REGARMA models) are often considered, in order to reflect a serial correlation among observations. In this article, we focus on an adaptive least absolute shrinkage and selection operator (LASSO) (ALASSO) method for the variable selection of the REGARMA models and extend it to the linear regression models with the ARMA-generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) errors (REGARMA-GARCH models). This attempt is an extension of the existing ALASSO method for the linear regression models with the AR errors (REGAR models) proposed by Wang et al. in 2007. New ALASSO algorithms are proposed to determine important predictors for the REGARMA and REGARMA-GARCH models. Finally, we provide the simulation results and real data analysis to illustrate our findings.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::577c3fa0aa4afda8ed3eb43860813287 https://doi.org/10.1080/03610918.2015.1096372 Zobrazit plný text záznamu