Expectation Maximization algorithm and its minimal detectable outliers

被引：12
|
作者：
Koch, Karl-Rudolf [1 ]
机构：
[1] Univ Bonn, Inst Geodesy & Geoinformat, Nussallee 17, D-53115 Bonn, Germany
来源：
STUDIA GEOPHYSICA ET GEODAETICA | 2017年 / 61卷 / 01期
关键词：
mean-shift model; variance-inflation model; hypothesis test; t-distribution; mixture of normal distributions; Monte Carlo method; MAXIMUM-LIKELIHOOD; ROBUST ESTIMATION; EM ALGORITHM;
D O I：
10.1007/s11200-016-0617-y
中图分类号：
P3 [地球物理学]; P59 [地球化学];
学科分类号：
0708 ; 070902 ;
摘要：
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.
引用
收藏
页码：1 / 18
页数：18
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