Robust maximum likelihood estimation for stochastic state space model with observation outliers

作     者：AlMutawa, J. 

作者机构：King Fahd Univ Petr & Minerals Dept Math & Stat Dhahran 31261 Saudi Arabia 

出 版 物：《INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE》 (国际系统科学杂志)

年 卷 期：2016年第47卷第11期

页      面：2733-2744页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：EM algorithm weighted likelihood estimation trimmed maximum likelihood estimation parallel algorithms maximum likelihood estimation randomised algorithm outliers stochastic state space model 

摘      要：The objective of this paper is to develop a robust maximum likelihood estimation (MLE) for the stochastic state space model via the expectation maximisation algorithm to cope with observation outliers. Two types of outliers and their influence are studied in this paper: namely,the additive outlier (AO) and innovative outlier (IO). Due to the sensitivity of the MLE to AO and IO, we propose two techniques for robustifying the MLE: the weighted maximum likelihood estimation (WMLE) and the trimmed maximum likelihood estimation (TMLE). The WMLE is easy to implement with weights estimated from the data;however, it is still sensitive to IO and a patch of AO outliers. On the other hand, the TMLE is reduced to a combinatorial optimisation problem and hard to implement but it is efficient to both types of outliers presented here. To overcome the difficulty, we apply the parallel randomised algorithm that has a low computational cost. A Monte Carlo simulation result shows the efficiency of the proposed algorithms.