Parameter Estimation for a Mixture of Two Univariate Gaussian Distributions: A Comparative Analysis of The Proposed and Maximum Likelihood Methods
Cliff Richard Kikawa, Michael Yu Shatalov, Petrus Hendrik Kloppers, Andrew Mkolesia
Journal of Advances in Mathematics and Computer Science · pp. 1–8 · Published 21 Sep 2015
10.9734/BJMCS/2016/16617Abstract
Two approaches to parameter estimation for a mixture of two univariate Gaussian distributions are numerically compared. The proposed method (PM) is based on decomposing a continuous function into its odd and even components and estimating them as polynomials, the other is the usual maximum likelihood (ML) method via the expected maximisation (EM) algorithm. An overlapped mixture of two univariate Gaussian distributions is simulated. The PM and ML are used to re-estimate the known mixture model parameters and the measure of performance is the absolute percentage error. The PM produces comparable results to those of to the ML approach. Given that the PM produces good estimates, and knowing that the ML always converges given good initial guess values (IGVs), it is thus recommended that the PM be used symbiotically with the ML to provide IGVs for the EM algorithm.
Cited by 4
Ahmed S. Almalki, Marcel M. El Hajj, Kasper Johansen · Frontiers in Remote Sensing · 2026
A. C. Mkolesia, C. Kikawa, M. Shatalov · 2016
C. Kikawa, M. Shatalov, B. Kalema · 2016
Hermine Biermé, Camille Constant, Anne Duittoz · The International Journal of Biostatistics · 2021
Related research
- Detecting Non-negligible New Influences in Environmental Data via a General Spatio-temporal Autoregressive Model — shares topic coverage
- Clusterization for Distributed Timely Detection of Changes in Smart Grids — shares topic coverage
- Estimation of Logistic Parameters Using a Fuzzy Least-squares Method and Different Types of Moments — shares topic coverage
- A Comparison between Fuzzy Log-logistic Parameters Estimators and Other Estimators — shares topic coverage
- Analysis of Production Relationships in the Capture Fishery of the Middle Cross River Basin: A Stochastic Frontier Approach — shares topic coverage
Article metrics
Real usage data collected on this platform.
0
Page views
0
PDF downloads
0
Outbound clicks
4
Citations
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
No views recorded yet.
Traffic sources
Referring site, by host.
No traffic recorded yet.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.