A New Algorithm to Estimate the Parameters of Log-Logistic Distribution Based on the Survival functions | Zendy

Ibrahim k. Amina | Zendy; Bayda Atiya Kalaf | Zendy

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A New Algorithm to Estimate the Parameters of Log-Logistic Distribution Based on the Survival functions

Author(s) -

Ibrahim k. Amina,

Bayda Atiya Kalaf

Publication year - 2021

Publication title -

journal of physics conference series

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.21

H-Index - 85

eISSN - 1742-6596

pISSN - 1742-6588

DOI - 10.1088/1742-6596/1879/3/032037

Subject(s) - maximum likelihood , estimation theory , likelihood function , estimator , mathematics , expectation–maximization algorithm , statistics , maximum likelihood sequence estimation , estimation , restricted maximum likelihood , algorithm , logistic function , simplex algorithm , logistic distribution , simplex , logistic regression , mathematical optimization , engineering , combinatorics , linear programming , systems engineering

Estimation of the parameters is quite important in the numerous fields for the development of mathematical models. Maximum likelihood estimation is a good method, which is usually used to elaborate on the parameter estimation. The likelihood function formed for the parameter estimation of Log-Logistic is very hard to maximize. Therefore, this paper proposes a new hybrid of Maximum Likelihood Estimator (MLE) and Simplex Downhill Algorithm (SDA) called (MLESDA) to estimate parameters of Log-Logistic distribution based on Survival functions. To compare the suggested method (MLESDA) and classical Maximum Likelihood (MLE) method, simulation is used. The results demonstrate that MLESDA is more efficient than the MLE method.

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