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Journal of Applied Mathematics and Computation

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Article Open Access http://dx.doi.org/10.26855/jamc.2018.10.002

Parameter Estimation with Least-Squares Method for the Inverse Gaussian distribution Model Using Simplex and Quasi-Newton Optimization Methods

Khizar Hayat Khan

Department of Mathematics, College of Science and Humanities, Prince Sattam Bin Abdulaziz University, Al-Kharj, Kingdom of Saudi Arabia.

*Corresponding author: Khizar Hayat Khan

Published: October 31,2018

Abstract

We find Survival rate estimates; parameter estimates for the inverse Gaussian distribution model using least-squares estimation method. We found these estimates for the case when partial derivatives were available and for the case when partial derivatives were not available. The simplex optimization (Nelder and Mead, and Hooke and Jeeves) methods were used for the case when first partial derivatives were not available and the Quasi – Newton optimization (Davidon-Fletcher-Powel (DFP) and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) methods were applied for the case when first partial derivatives were available. The medical data sets of 21 Leukemia cancer patients with time span of 35 weeks were used.

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How to cite this paper

Parameter Estimation with Least-Squares Method for the Inverse Gaussian distribution Model Using Simplex and Quasi-Newton Optimization Methods

How to cite this paper: Khizar Hayat Khan. (2018) Parameter Estimation with Least-Squares Method for the Inverse Gaussian distribution Model Using Simplex and Quasi-Newton Optimization MethodsJournal of Applied Mathematics and Computation2(10), 466-472.

DOI: http://dx.doi.org/10.26855/jamc.2018.10.002