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

fYear

2012

fDate

3-5 Sept. 2012

Firstpage

864

Lastpage

869

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/CONTROL.2012.6334745

Filename

6334745