Comparison between some Parametric Robust Methods for Estimating the Parameters of the Multiple Normal Distribution

Authors

  • M. Murtadha Mansour Abdullah

Abstract

    Estimating the parameters of the Multivariate Normal Distribution is very important process in many statistical Application like the Principal Component Analysis or Canonical Analysis. The paper aims at finding robust and efficient estimators for the parameters of the multivariate normal distribution by using parametric method which is the reweighted minimum vector method RMV and compare it with another robust estimation methods like S estimation method in case of deferent sample sizes and deferent contaminated ratios. Results shows that the reweighted minimum vector is the best method via simulation and real data was taken from waist sewerage directorate minimum esquire error (MMSE) was used as a comparison tool between the two estimation methods.

 

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References

- Ahmed, A. B. (2008). Standard Modeling of National Energy Consumption in Algeria during the Period (1988: 10-2007: 03). (Master Unpublished). University of Algeria, Faculty of Economic Sciences and Management Sciences.

- Olive, D. J. (2018) .Robust Multivariate analysis .springer.

-Ali, H. Yahiya, s. s. s. & Omar, Z. (2014, June). The efficiency of reweighted minimum vector variance. In AIP conference proceedings (Vol. 1602, NO. 1, pp.1151-1156).AIP.

- Bable, B., & Pawar, D. (2012). Vector time series: the concept and properties to the vector staionary time series. International Research Journal of Agricultural Economics and Statistics, 3(1), 84-95.

- Verardi, V. & Mc Cathie, A. (2012). The S-estimator of multivariate Location and Scatter in Stata. Stata journal, 12(2), 299

- Huang, S.-C. (2008). Combining wavelet-based feature extractions with SVMs for financial time series forecasting. Journal of Statistics and Management Systems, 11(1), 37-48.

- Hossjer, O., Croux, C, & Rousseeuw, p. j. (1994) Asymptotic of generalized s-estimation journal of Multivariate Analysis, 51(1), 148-177.

-Croux, C. Rousseeuw, p. J & Hossjer, O. (1994). “Generalized s-estimator”, JASA, Vol. 89, NO. 428, 1271-1281.

-Bigot, J, Biscay, R. J.loubes, J. M. & Muniz-Alvarez, L. (2011) .Group lasso estimation of high-dimensional covariance matrices. Journal of Machine learning Research, 12(Nov), 3187-3225

-Chen, Y. , Wiesel, A. Eldar, Y. C. , & Hero, A. O. (2010). Shrinkage algorithms for MMSE covariance estimation. IEEE Transactions on signal processing, 58

(10),5016-5029

- Iman, T. M. (2014). Standard analytical study of family consumption of electricity - Study of the case of Sonelgaz Unit Al Buira - during the period 2008:1 - 2013:12 (Vol. 18). Ministry of Higher Education and Scientific Research University of Akli Mahnood Olhad/Faculty of Economic and Commercial Sciences and Management Sciences.

Published

2022-12-14

How to Cite

M. Murtadha Mansour Abdullah. (2022). Comparison between some Parametric Robust Methods for Estimating the Parameters of the Multiple Normal Distribution. Al Kut Journal of Economics and Administrative Sciences, 14(45). Retrieved from https://kjeas.uowasit.edu.iq/index.php/kjeas/article/view/481