Modified Maximum Likelihood Estimation in Autoregressive Processes with Generalized Exponential Innovations  

作　　者：Bernardo Lagos-álvarez Guillermo Ferreira Emilio Porcu 

机构地区：[1]Department of Statistics, Universidad de Concepción, Concepción, Chile [2]Department of Mathematics, University Federico Santa María, Valparaíso, Chile

出　　处：《Open Journal of Statistics》2014年第8期620-629,共10页统计学期刊（英文）

摘　　要：We consider a time series following a simple linear regression with first-order autoregressive errors belonging to the class of heavy-tailed distributions. The proposed model provides a useful generalization of the symmetrical linear regression models with independent error, since the error distribution covers both correlated innovations following a Generalized Exponential distribution. Furthermore, we derive the modified maximum likelihood (MML) estimators as an efficient alternative for estimating model parameters. Finally, we investigate the asymptotic properties of the proposed estimators. Our findings are also illustrated through a simulation study.We consider a time series following a simple linear regression with first-order autoregressive errors belonging to the class of heavy-tailed distributions. The proposed model provides a useful generalization of the symmetrical linear regression models with independent error, since the error distribution covers both correlated innovations following a Generalized Exponential distribution. Furthermore, we derive the modified maximum likelihood (MML) estimators as an efficient alternative for estimating model parameters. Finally, we investigate the asymptotic properties of the proposed estimators. Our findings are also illustrated through a simulation study.

关 键 词：AUTOREGRESSIVE Time Series Model MAXIMUM LIKELIHOOD MODIFIED MAXIMUM LIKELIHOOD Least SQUARES Generalized EXPONENTIAL 

分 类 号：O1[理学—数学]