Notes on consistency of some minimum distance estimators with simulation results

Jitka Hrabáková () and Václav Kůs ()
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Jitka Hrabáková: Czech Technical University in Prague
Václav Kůs: Czech Technical University in Prague

Metrika: International Journal for Theoretical and Applied Statistics, 2017, vol. 80, issue 2, No 7, 243-257

Abstract: Abstract We focus on the minimum distance density estimators $${\widehat{f}}_n$$ f ^ n of the true probability density $$f_0$$ f 0 on the real line. The consistency of the order of $$n^{-1/2}$$ n - 1 / 2 in the (expected) L $$_1$$ 1 -norm of Kolmogorov estimator (MKE) is known if the degree of variations of the nonparametric family $$\mathcal {D}$$ D is finite. Using this result for MKE we prove that minimum Lévy and minimum discrepancy distance estimators are consistent of the order of $$n^{-1/2}$$ n - 1 / 2 in the (expected) L $$_1$$ 1 -norm under the same assumptions. Computer simulation for these minimum distance estimators, accompanied by Cramér estimator, is performed and the function $$s(n)=a_0+a_1\sqrt{n}$$ s ( n ) = a 0 + a 1 n is fitted to the L $$_1$$ 1 -errors of $${\widehat{f}}_n$$ f ^ n leading to the proportionality constant $$a_1$$ a 1 determination. Further, (expected) L $$_1$$ 1 -consistency rate of Kolmogorov estimator under generalized assumptions based on asymptotic domination relation is studied. No usual continuity or differentiability conditions are needed.

Keywords: Minimum distance estimators; Consistency; Kolmogorov distance; Degree of variations; 62G20; 62G05 (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s00184-016-0601-0

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