A new type of parameter estimation algorithm for missing data problems

Petre Stoica, Luzhou Xu and Jian Li

Statistics & Probability Letters, 2005, vol. 75, issue 3, 219-229

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.

Keywords: Parameter; estimation; with; missing; data; Maximum; likelihood; Expectation-maximization; Cyclic; maximization (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (2)

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