An alternative perspective on the mixture estimation problem
Nagode, Marko (Author), Fajdiga, Matija (Author)

URLURL - Presentation file, Visit http://dx.doi.org/10.1016/j.ress.2005.02.005 This link opens in a new window

The paper presents an alternative perspective on the mixture estimation problem. First, observations are counted into a histogram. Secondly, rough andenhanced parameter estimation followed by the separation of observations isdone. Finally, the residue is distributed between the components by the Bayes decision rule. The number of components, the mixture component parameters and the component weights are modelled jointly, no initial parameter estimates are required, the approach is numerically stable, the number of components has no influence upon the convergence and the speed of convergence is very high. The alternative perspective is compared to the EM algorithm and verified through several data sets. The presented algorithm showed significant advantages compared to the competitive methods and has already been successfully applied in reliability and fatigue analyses.

Keywords:mixture distributions, predictive distributions, normal mixtures, mixture component, parameter estimation, EM algorithm
Work type:Not categorized (r6)
Tipology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Number of pages:str. 388-397
Numbering:Letn. 91, št. 4
ISSN on article:0951-8320
COBISS.SI-ID:8347163 Link is opened in a new window
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Record is a part of a journal

Title:Reliability engineering & systems safety
Shortened title:Reliab. eng. syst. saf.
Publisher:Elsevier Applied Science
COBISS.SI-ID:27385344 This link opens in a new window

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