Impact of Missing Data on EM Algorithm under Rayleigh Distribution

Zhendong Li, Mengmeng Li

Abstract


Is EM algorithm parameter estimation under Rayleigh distribution sensitive to missing data and if it is, what extent is it? By designing computer simulation methods, contrast and analyze the results of maximum likelihood estimation with complete data and EM algorithm estimation under different missing rate in small sample. It shows that the results were almost identical when the missing rate is below 0.30, but the efficiency of EM parameter estimation gradually deteriorates as the missing rate increases. Meanwhile the results also show that the EM algorithm is sensitive to sample size and the selection of initial value.

 

DOI : http://dx.doi.org/10.11591/telkomnika.v12i6.5491


Full Text:

PDF

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

shopify stats IJEECS visitor statistics