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https://doi.org/10.1109/SSP.2014.6884664

has been cited by the following article:

• TITLE: Robust Estimation of the Normal-Distribution Parameters by Use of Structural Partitioning-Perobls D Method

  AUTHORS: Gligorije Perović

  KEYWORDS: Non-Homogeneous Sets of Observations, Tukeyan Mixed-Distributions, Robust Perobls D Method

  JOURNAL NAME: American Journal of Computational Mathematics, Vol.9 No.4, December 17, 2019

  ABSTRACT: Quite many authors have dealt with the estimation of the parameters of normal distribution on the basis of non-homogeneous sets: Hald A. 1949 [1], Arango-Castillo L. and Takahara G. 2018 [2]. All the robust methods are based on the assumption that the results affected by gross errors can be found to the left and/or to the right of censoring, or truncated, points. However, as a rule, the (intrinsic) distribution of observations is complex (mixed) consisting of two or more distributions. Then the existing methods, such as ML, Huber’s, etc., yield enlarged estimates for the normal-distribution variance. By studying better estimates the present author has invented new method, called PEROBLS D, based on the Tukeyan mixed-distribution model in which both the contamination rate (percentage) and the parameters of both distributions, forming the mixed one, are estimated, and for the parameters of the basic normal distribution better estimates are obtained than by the existing methods.