International Journal of Mathematics and Computational Science
Articles Information
International Journal of Mathematics and Computational Science, Vol.5, No.1, Mar. 2019, Pub. Date: Apr. 17, 2019
A Suggested Method from MAD-Median and Ridge Regression Estimator
Pages: 1-5 Views: 69 Downloads: 31
[01] Irtefaa A. Neamah, Department of Mathematics, University of Kufa, Najaf, Iraq.
For the important of the estimation strategies and its big role in the sciences, we look forward to developing a suggested method based on the other classical methods. This is the main aim of this paper. One of the most important statistical distributions is Weibull distribution. So, we look forward to study more strategies to estimate its parameters shape and scale. In this paper, we study two known methods in estimation. There are ridge regression RR and Mad/Median estimator. But, we decided to create a new suggested method to know what is the best of them. So, the suggested method is a composition between the ridge regression and the mad/median estimator. To know the best result, it be supported this suggestion by a simulation study. Besides that, the comparison based on the most uses criteria is mean squares error MSE. The result was the suggested method gives more efficient result at most of the sample sizes.
Estimation, Ridge Regression, Mad/Median Estimator, Weibull Distribution, Mean Square Error, Simulation Study
[01] E. Horel and R. Kennard, "Ridge Regression: Applications to Nonorthogonal Problems," Technometrics, American Statistical Association, JSOR, USA., 1970.
[02] G. Stone and H. Heeswijk R. G., "Parameter Estimation for The Weibull Distribution," IEEE Transactions on Reliability. Electr. Insul, pp. Vol EI- 12 No. 4, August 1977.
[03] M. Gallagher and A. H. Moore, "Robust Minimum Distance Estimation Using the 3-parameter Weibull distribution," IEEE Transactions on Reliability, pp. Vol. 39, No. 5, 1990.
[04] M. Cacciari, G. Mazzanti, G. Montanari and J. Jacquelin, "A Robust Technique for the estimation two-Parameter Weibull Function for Complete Data Sets," Manscript in Italy and France, 2002.
[05] K. Sultan, M. Mahmoud and H. Saleh, "Estimation of Parameters of the Weibull Distribution Based on Progressively Censored Data," International Mathematical Forum, pp. 2, No. 41, 2031-2048, 2007.
[06] Z. ZANGIN, On Median and Ridge Estimation of SURE Models, Jönköping: doctoral dissertation at Jönköping International Business School, 2012.
[07] E. A. Hefnawy and F. A, "A combined nonlinear programming model and Kibria method for choosing ridge parameter regression," Communications in Statistics – Simulation and Computation,, Vols. 43 (6),. doi, 2013.
[08] M. Aslam, "Performance of Kibria's method for the heteroscedastic ridge regression model, Some Monte Carlo evidence’. Communications in Statistics – Simulation and Computation, Vol. 43 (4), 673-686. doi, 2014.
[09] D. P. Kafi, A. Robiah and A. R. Bello, "Using Ridge Least Median Squares to Estimate the Parameter by Solving Multicollinearity and Outliers Problems," Modern Applied Science, Vols. Vol. 9, No. 2, no. ISSN 1913-1844 E-ISSN 1913-1852, pp. 191-198, 2015.
[10] B. Kibria and S. Banik, "Some Ridge Regression Estimators and Their Performances," Journal of Modern Applied Statistical Methods, Vols. Vol. 15, No. 1, 206-238., no. ISSN 1538 − 9472, May, 2016.
[11] H. Renne, The Weibull Distribution, A Handbook, Giessen, Germany: Taylor & Francis Group, LLC, 2009.
[12] D. J. Olive, Applied Robust Statistics, Southern Illinois University: Department of Mathematics, 2008.
[13] Rashwan and M. E.-D. a. N. I., "Solving Multicollinearity Problem Using Ridge Regression Models," Int. J. Contemp. Math. Sciences, Vol. 6, No. 12, 585., 2011.
[14] H. AHMED, K. ALANI ISAM and B. A. A. A., "Using Simulation In Teaching Simple Linear Regression," Journal of Anbar University Pure Sciences, vol. 2 No. 3, no. ISSN: 1991-8941, 2008.
MA 02210, USA
AIS is an academia-oriented and non-commercial institute aiming at providing users with a way to quickly and easily get the academic and scientific information.
Copyright © 2014 - American Institute of Science except certain content provided by third parties.