Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10526159 | Statistics & Probability Letters | 2005 | 11 Pages |
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 communication 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 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.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Petre Stoica, Luzhou Xu, Jian Li,