Please use this identifier to cite or link to this item:
Title: Estimating parameters in autoregressive models with asymmetric innovations
Authors: Wong, W.-K. 
Bian, G.
Keywords: Autoregression
Generalized logistic distribution
Least squares
Modified maximum likelihood
Issue Date: 2005
Citation: Wong, W.-K., Bian, G. (2005). Estimating parameters in autoregressive models with asymmetric innovations. Statistics and Probability Letters 71 (1) : 61-70. ScholarBank@NUS Repository.
Abstract: Tiku et al. (Theory Methods 28(2) (1999) 315) considered the estimation in a regression model with autocorrelated error in which the underlying distribution be a shift-scaled Student's t distribution, developed the modified maximum likelihood (MML) estimators of the parameters and showed that the proposed estimators had closed forms and were remarkably efficient and robust. In this paper, we extend the results to the case, where the underlying distribution is a generalized logistic distribution. The generalized logistic distribution family represents very wide skew distributions ranging from highly right skewed to highly left skewed. Analogously, we develop the MML estimators since the ML (maximum likelihood) estimators are intractable for the generalized logistic data. We then study the asymptotic properties of the proposed estimators and conduct simulation to the study. © 2004 Elsevier B.V. All rights reserved.
Source Title: Statistics and Probability Letters
ISSN: 01677152
DOI: 10.1016/j.spl.2004.10.022
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.