Refrences:
1.Pareto, V. The new theories of economics", J. Polit. Econ., 5(4), pp. 485-502 (1897).
2. Munir, R., Saleem, M., Aslam, M., and Ali, S. Comparison of di_erent methods of parameters estimation for Pareto model", Casp. J. Appl. Sci. Res., 2(1), pp. 45-56 (2013).
3. Arnold, B.C. Encyclopaedia of Statistical Sciences, John Wiley (2008). 4. Abdel-All, N.H., Mahmoud, M.A.W., and Abd-Ellah, H.N. Geometrical properties of Pareto distribution", Appl. Math. Comput., 145(2), pp. 321-339 (2003). 5. Sankaran, P.G. and Nair, M.T. On _nite mixture of Pareto distributions", Calcutta Stat. Assoc. Bull., 57(1-2), pp. 225-226 (2005). 6. Burroughs, S.M. and Tebbens, S.F. Upper-truncated power law distributions", Fractals, 9(1), pp. 209-222 (2001). 7. Castillo, E. and Hadi, A.S. Fitting the generalized Pareto distribution to data", J. Am. Stat. Assoc., 92(440), pp. 1609-1620 (1997). 8. Bourguignon, M., Ghosh, I., and Cordeiro, G.M. General results for the transmuted family of distributions and new models", J. Probab. Stat., 2016, pp. 1-12 (2016). 9. Quandt, R.E. Old and new methods of estimation and the Pareto distribution", Metrika, 10(1), pp. 55- 82 (1966). 10. A_fy, E.E. Order statistics from Pareto distribution", J. Appl. Sci., 6(10), pp. 2151-2157 (2006). 11. Lu, H.-L. and Tao, S.H. The estimation of Pareto distribution by a weighted least square method", Qual. Quant., 41(6), pp. 913-926 (2007). 12. Pobo_c__kov_a, I. and Sedlia_ckov_a, Z. Comparison of four methods for estimating the Weibull distribution parameters", Appl. Math. Sci., 8(83), pp. 4137-4149 (2014). 13. Shawky, A.I. and Abu-Zinadah, H.H. Exponentiated Pareto distribution: di_erent method of estimations", Int. J. Contemp. Mathematical Sci., 4(14), pp. 677-693 (2009). 14. Grimshaw, S.D. Computing maximum likelihood estimates for the generalized Pareto distribution", Technometrics, 35(2), pp. 185-191 (1993). 15. Cohen, A.C. and Whitten, B. Modi_ed maximum likelihood and modi_ed moment estimators for the three-parameterWeibull distribution", Commun. Stat. Methods, 11(23), pp. 2631-2656 (1982). S. Haider Bhatti et al./Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 605{614 613 16. Iwase, K. and Kanefuji, K. Estimation for 3- parameter lognormal distribution with unknown shifted origin", Stat. Pap., 35(1), pp. 81-90 (1994). 17. Lalitha, S. and Mishra, A. Modi_ed maximum likelihood estimation for Rayleigh distribution", Commun. Stat. - Theory Methods, 25(2), pp. 389-401 (1996). 18. Rashid, M.Z. and Akhter, A.S. Estimation accuracy of exponential distribution parameters", Pakistan J. Stat. Oper. Res., 7(2), pp. 217-232 (2011). 19. Zaka, A. and Akhter, A.S. Modi_ed moment, maximum likelihood and percentile estimators for the parameters of the power function distribution", Pakistan J. Stat. Oper. Res., 10(4), pp. 361-368 (2014). 20. Fisher, R.A. On the mathematical foundations of theoretical statistics", Philos. Trans. R. Soc. London. Ser. A, Contain. Pap. a Math. or Phys. Character, 222, pp. 309-368 (1922). 21. Cohen, C.A. and Whitten, B.J. Modi_ed moment and maximum likelihood estimators for parameters of the three-parameter Gamma distribution", Commun. Stat. Comput., 11(2), pp. 197-216 (1982). 22. Al-Fawzan, M.A., Methods for Estimating the Parameters of the Weibull Distribution, King Abdulaziz City Sci. Technol. (2000). 23. Khalaf, G., Mansson, K., and Shukur, G. Modi_ed ridge regression estimators", Commun. Stat. - Theory Methods, 42(8), pp. 1476-1487 (2013). 24. Aydin, D. and S_eno_glu, B. Monte Carlo comparison of the parameter estimation methods for the twoparameter Gumbel distribution", J. Mod. Appl. Stat. Methods, 14(2), pp. 123-140 (2015). 25. Shakeel, M., Haq, M.A. ul, Hussain, I., Abdulhamid, A.M., and Faisal, M. Comparison of two new robust parameter estimation methods for the power function distribution", PLoS ONE, 11(8), p. e016069 (2016). 26. James, W. and Stein, C. Estimation with quadratic loss", Proc. Fourth Berkeley Symp. Math. Stat. Probab., 1(1961), pp. 361-379 (1961). 27. Team, R.C.R, A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria (2016). 28. Clark, D.R., A Note on the Upper-truncated Pareto Distribution, Casualty Actuarial Society E-Forum, Winter, pp. 1-22 (2013). 29. Kantar, Y.M. Generalized least squares and weighted least squares estimation methods for distributional parameters", REVSTAT - Stat. J., 13(3), pp. 263-282 (2015). 30. Beirliant, J., Teugels, J.L., and Vynckier, P., Practical Analysis of Extreme Values, Leuven University Press, Leuven, Belgium (1996). 31. Obradovi_c, M. On Asymptotic e_ciency of goodness of _t tests for Pareto distribution based on characterizations", Filomat, 29(10), pp. 2311-2324 (2015). 32. Kolmogorov, A. On the determination of empirical _t of a distribution" [Sulla determinazione empirica di una legge di distribuzione], G. dell'Istituto Ital. degli Attuari, 4, pp. 83-91 (1933). 33. Smirnov, N.V. Estimate of deviation between empirical distribution functions in two independent samples", Bull. Moscow Univ., 2, pp. 3-16 (1939).
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