DocumentCode
493622
Title
Asymptotic Normality of Parametric Estimators of Markov Chain Vector Dependence Models
Author
Yi Wende ; Wei Gui-wu
Author_Institution
Dept. of Math. & Stat., Chongqing Univ. of Arts & Sci., Chongqing
Volume
2
fYear
2009
fDate
7-8 March 2009
Firstpage
146
Lastpage
152
Abstract
We consider an alternative approach based on copulas to investigate dependence structures of stationary Markov type time series vector. Based on the properties of parametric estimators of the 2SPMLE, we propose a method of parametric estimation of three-stage pseudo maximum likelihood estimation and investigate the asymptotic normality of parametric estimators.
Keywords
Markov processes; maximum likelihood estimation; Markov chain vector dependence models; asymptotic normality; parametric estimators; stationary Markov type time series vector; three-stage pseudomaximum likelihood estimation; Art; Computer science; Computer science education; Continuing education; Educational technology; Mathematics; Maximum likelihood estimation; Parameter estimation; Parametric statistics; Random variables; 3SPMLE; Asymptotic normality; Contemporaneous dependence; Copula; Temporal dependence;
fLanguage
English
Publisher
ieee
Conference_Titel
Education Technology and Computer Science, 2009. ETCS '09. First International Workshop on
Conference_Location
Wuhan, Hubei
Print_ISBN
978-1-4244-3581-4
Type
conf
DOI
10.1109/ETCS.2009.294
Filename
4959008
Link To Document