MAXIMUM LIKELIHOOD BASED ON NEWTON RAPHSON, FISHER SCORING AND EXPECTATION MAXIMIZATION ALGORITHM APPLICATION ON ACCIDENT DATA.
- Faculty of Math and Science Universitas Sumatera Utara.
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The parameter estimate is the value of the parameter based on data or samples taken from a certain popolation. There are several methods to estimate the parameters of one of them is Maximum Likelihood Estimation (MLE). MLE is a distribution approach by maximizing likelihood function. The purpose of this study is to estimate the parameter value of a data distributed with Maximum Likelihood based on the iteration algorithm. The iteration algorithm that will be used is Newton Raphson, Fisher Scoring and Expectation Maximization Algorithm with the help of Matlab 2016a. The purpose of this paper is to look at the parameter values of three algorithms that have the same results or have great results and with regard to the number of iterations performed by the three algorithms. In this paper the three algorithms will be applied to the accident data.
- Bain, J., dan Engelhardt, M. (1992). Introduction to probability and mathematical statistics 2nd edition. Duxbury. USA.
- Bolstad, B.M. (1998). Comparing some iterative method of parameter estimation for censored gamma data. The University of Waikato.
- Chan, S. H. (2015). Expectation maximization algorithm. University PURDUE.
- Ehlers, R. (2002). Maximum likelihood estimation prosedures for categorial data. University of Pretoria, South Africa.
- Kulter, M. dkk. (2004). Applied linear regression model. Mc Graw Hill/ Irwin Series: Operation and Decision.
- Lange, K. (2004). Optimization. Springer Texts in Statistics, USA
- Mai, A.T . dkk. (2014). On optimization algorithms for maximum likelihood estimation. CIRRELT, 64.
- Miller, R. B. (2011). Maximum likelihood estimation and inference with example in R, SAS and ADMB 1st edition. John Wiley and Sons Ltd. United Kingdom.
- Storvik, G. (2011). Numerical optimization of likelihoods: additiona literature for STK2120. University of Oslo.
- Wu,J . C. (1983). On convergence properties of the EM algorithm. University of Wisconsin. The Annals of Statistics. Vol.11, No. 1,95-103.
[Switamy Angnitha Purba, Sutarman and Open Darnius. (2018); MAXIMUM LIKELIHOOD BASED ON NEWTON RAPHSON, FISHER SCORING AND EXPECTATION MAXIMIZATION ALGORITHM APPLICATION ON ACCIDENT DATA. Int. J. of Adv. Res. 6 (Jan). 965-969] (ISSN 2320-5407). www.journalijar.com
University of Sumatera Utara
