DocumentCode :
2939572
Title :
Parameter estimation with missing data via equalization-maximization
Author :
Stoica, Petre ; Xu, Luzhou ; Li, Jian
Author_Institution :
Dept. of Inf. Technol., Uppsala Univ., Sweden
Volume :
4
fYear :
2005
fDate :
18-23 March 2005
Abstract :
The expectation-maximization (EM) algorithm is often used in maximum likelihood (ML) estimation problems with missing data. However, EM can be rather slow to converge. In this paper, we introduce a new algorithm for parameter estimation problems with missing data, which we call equalization-maximization (EqM) (for reasons to be explained later). We derive the EqM algorithm in a general context and illustrate its use in the specific case of a Gaussian autoregressive time series with a varying amount of missing observations. In the presented examples, EqM outperforms EM in terms of computational speed, at a comparable estimation performance.
Keywords :
Gaussian distribution; autoregressive processes; maximum likelihood estimation; optimisation; time series; EqM; Gaussian autoregressive time series; equalization-maximization; maximum likelihood estimation; missing data; missing observations; parameter estimation; Convergence; Councils; Information technology; Iterative algorithms; Maximum likelihood estimation; Parameter estimation; Probability density function; Virtual reality;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on
ISSN :
1520-6149
Print_ISBN :
0-7803-8874-7
Type :
conf
DOI :
10.1109/ICASSP.2005.1415944
Filename :
1415944
Link To Document :
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