References
- Fisher RA, Tippett LHC. On the estimation of the frequency distributions of the largest or smallest member of a sample. Proc Camb Philos Soc. 1928;24:180–190.
- Shenton LR, Bowman KO. Maximum likelihood estimation in small samples. London: C. Griffin; 1977.
- Cohen CA, Whitten B. Modified maximum likelihood and modified moment estimators for the three-parameter Weibull distribution. Commun in Stat – Theory Methods. 1982;11:2631–2656.
- Koch SP. Bias error in maximum likelihood estimation. J Hydrol (Amst). 1991;122:289–300.
- Kimball BF. The bias in certain estimates of the parameters of the extreme-value distribution. Ann Math Stat. 1956;27:758–767.
- Firth D. Bias reduction of maximum likelihood estimates. Biometrika. 1993;80:27–38.
- Ross R. Formulas to describe the bias and standard deviation of the ML-estimated Weibull shape parameter. IEEE Trans Dielectr Electr Insul. 1994;1:247–253.
- Jacquelin J. Generalization of the method of maximum likelihood (insulation testing). IEEE Trans Electr Insul. 1993;28:65–72.
- Engelhardt M, Bain LJ. Some results on point estimation for the two-parameter Weibull or extreme-value distribution. Technometrics. 1974;16:49–56.
- Engelhardt M, Bain LJ. Simplified statistical procedures for the Weibull or extreme-value distribution. Technometrics. 1977;19:323–331.
- Cacciari M, Mazzanti G, Montanari GC. Comparison of maximum likelihood unbiasing methods for the estimation of the Weibull parameters. IEEE Trans Dielectr Electr Insul. 1996;3:18–27.
- Loganathan A, Uma M. Comparison of estimation methods for inverse Weibull parameter. Global Stoch Anal. 2017;4:83–93.
- Mazucheli J, Menezes AFB, Dey S. Bias-corrected maximum likelihood estimators of the parameters of the inverse Weibull distribution. Commun Stat – Simul Comput. 2019;48:2046–2055.
- Fiorentino M, Gabriele S. A correction for the bias of maximum-likelihood estimators of gumbel parameters. J Hydrol (Amst). 1984;73:39–49.
- Phien HN. A review of methods of parameter estimation for the extreme value type-1 distribution. J Hydrol (Amst). 1987;90:251–268.
- Nelder JA, Mead R. A simplex method for function minimization. Comput J. 1965;7:308–313.
- Kai-tai F, Yuan W. A sequential algorithm for solving a system of nonlinear equations. J Comput Math. 1991;9:9–16.
- Nash JC, Walker-Smith M. Nonlinear parameter estimation: an integrated system in BASIC. New York: M. Dekker; 1987.
- Fang KT, Wang Y, Bentler PM. Some applications of number-theoretic methods in statistics. Stat Sci. 1994;9(3):416–428.
- Halton JH. On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals. Numer Math. 1960;2:84–90.
- Fang KT, Wang Y. Number-theoretic methods in statistics. London: Chapman and Hall; 1994.
- Thoman DR, Bain LJ, Antle CE. Inferences on the parameters of the Weibull distribution. Technometrics. 1969;11:445–460.
- Cleveland WS. Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc. 1979;74:829–836.
- Cleveland WS. LOWESS: A program for smoothing scatterplots by robust locally weighted regression. Am Stat. 1981;35:54.
- Cleveland WS, Grosse E, Shyu WM. Local regression models Stat Models in S. New York: Routledge; 2017. p. 309–376.
- Efron B. Bootstrap methods: another look at the jackknife. Ann Stat. 1979;7:1–26.
- Lieblein J, Zelen M. Statistical investigation of the fatigue life of deep-groove ball bearings. J Res Natl Bur Stand (1934). 1956;57:273.
- Efron B, Tibshirani RJ. An introduction to the bootstrap. Boston (MA): Springer US; 1993.