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
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;
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
DOI :
10.1109/ETCS.2009.294
