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Estimation of parameter in Lehmann-Cox power-law model by minimizing Kolmogorov-Smirnov and Savage functionals
Engineering Education # 07, July 2012
DOI: 10.7463/0712.0410885
In this paper we consider two etimations of the unknown parameter in the Lehmann-Cox power-law model. These estimates are obtained by minimization of two different functionals: Kolmogorov-Smirnov and Savage ones. The type of these functionals was obtained by using non-parametric statistics proposed by authors in their previous articles. We demonstrated the advantage of one statistics over the other by use of the methods of statistical modeling when the Lehmann-Cox power-law model is valid. It was proposed using a correction coefficient calculated with Monte Carlo method to eliminate bias of estimates for small sample sizes.
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