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On fractal distribution function estimation and applications

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  • Stefano Maria Iacus

    ()

  • Davide La Torre

    ()

Abstract

In this paper we review some recent results concerning the approximations of distributionfunctions and measures on [0, 1] based on iterated function systems. The twodifferent approaches available in the literature are considered and their relations areinvestigated in the statistical perspective. In the second part of the paper we proposea new class of estimators for the distribution function and the related characteristicand density functions. Glivenko-Cantelli, LIL properties and local asymptotic minimaxeciency are established for some of the proposed estimators. Via Monte Carlo analysiswe show that, for small sample sizes, the proposed estimator can be as efficient oreven better than the empirical distribution function and the kernel density estimatorrespectively. This paper is to be considered as a first attempt in the construction ofnew class of estimators based on fractal objects. Pontential applications to survivalanalysis with random censoring are proposed at the end of the paper.

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File URL: http://wp.demm.unimi.it/tl_files/wp/2002/DEMM-2002_007wp.pdf
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Paper provided by Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano in its series Departmental Working Papers with number 2002-07.

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Date of creation: 01 Jan 2002
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Handle: RePEc:mil:wpdepa:2002-07

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  1. Stefano Maria Iacus & Davide La Torre, 2002. "Approximating distribution functions by iterated function systems," Departmental Working Papers 2002-03, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
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