Title :
Counter-examples design to global convergence of maximum likelihood estimators
Author :
Zou, Yiqun ; Tang, Xiafei ; Ding, Zhengtao
Author_Institution :
Dept. of Intell. Sci. & Technol., Central South Univ., Changsha, China
Abstract :
MLE(Maximum Likelihood Estimation) is widely applied in system identification because of its consistency, asymptotic efficiency and sufficiency. However gradient-based optimization of the likelihood function might end up in local convergence. To overcome this difficulty, the non-local-minimum conditions are very useful. Here we suggest a heuristic method of constructing local minimum examples for ARMAX, ARARMAX and BJ models. Based on them the derivation of non-local-minimum conditions can be inspired by analyzing these examples.
Keywords :
convergence of numerical methods; gradient methods; maximum likelihood estimation; optimisation; ARARMAX model; ARMAX model; BJ model; MLE consistency; MLE sufficiency; asymptotic efficiency; counter-example design; global convergence; gradient-based optimization; likelihood function; local convergence; local minimum example construction; maximum likelihood estimators; nonlocal minimum conditions; nonlocal-minimum condition derivation; system identification; Anodes;
Conference_Titel :
Control (CONTROL), 2012 UKACC International Conference on
Conference_Location :
Cardiff
Print_ISBN :
978-1-4673-1559-3
Electronic_ISBN :
978-1-4673-1558-6
DOI :
10.1109/CONTROL.2012.6334745
