Expectation Maximization algorithm and its minimal detectable outliers

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• 作者：Karl-Rudolf Koch
• 关键词：mean ; shift model ; variance ; inflation model ; hypothesis test ; t ; distribution ; mixture of normal distributions ; Monte Carlo method
• 刊名：Studia Geophysica et Geodaetica
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：61
• 期：1
• 页码：1-18
• 全文大小：
• 刊物类别：Earth and Environmental Science
• 刊物主题：Geophysics/Geodesy; Structural Geology; Atmospheric Sciences;
• 出版者：Springer Netherlands
• ISSN：1573-1626
• 卷排序：61

文摘

Minimal Detectable Biases (MDBs) or Minimal Detectable Outliers for the Expectation Maximization (EM) algorithm based on the variance-inflation and the mean-shift model are determined for an example. A Monte Carlo method is applied with no outlier and with one, two and three randomly chosen outliers. The outliers introduced are recovered and the corresponding MDBs are almost independent from the number of outliers. The results are compared to the MDB derived earlier by the author. This MDB approximately agrees with the MDB for one outlier of the EM algorithm. The MDBs for two and three outliers are considerably larger than MDBs of the EM algorithm.