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Journal of Applied Mathematics and Computation

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

Author:Khizar Hayat Khan Date:October 31,2018 Hits:

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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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, Department of Mathematics, College of Science and Humanities, Prince Sattam Bin Abdulaziz University, Al-Kharj, Kingdom of Saudi Arabia
Email: drkhizar@gmail.com, k.khan@psau.edu.sa

How to cite this paper: Khan, K. H. (2018) Parameter Estimation with Least-Squares Method for the Inverse Gaussian distribution Model Using Simplex and Quasi-Newton Optimization Methods. Journal of Applied Mathematics and Computation, 2(10), 466-472.
DOI: 10.26855/jamc.2018.10.002
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