On fractal distribution function estimation and applications
Theoretical background for affine IFS
Theorem 3 (Forte and Vrscay, 1995)
Theorem 4 (Iacus and La Torre, 2001).
Theorem 5 (Iacus and La Torre, 2001)
Theorem 8 (Forte and Vrscay, 1998)
Theorem 9 (Collage Theorem for FT, (Forte and Vrscay, 1998))
4.1 Asymptotic results for the quantile-based IFS estimator
Theorem 12 (Gill and Levit, 1995)
4.2 Characteristic function and Fourier density estimation
Let (Y, dY ) be a complete metric space. Given an
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suppose that there exists a contractive map f on Y with contractivity factor 0 ≤ c < 1 such that
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If
is the fixed point of
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then
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So if one wishes to approximate a function y with the fixed point
of an unknown contractive map f, it is only needed to solve the inverse problem of finding f which minimizes the collage distance dY (y,f(y)). The main result in Forte and Vrscay that we will use to build one of the IFSs estimators is that the inverse problem can be reduced to minimize a suitable quadratic form in terms of the pi given a set of affine maps wi and the sequence of moments gk of the target measure. Let

be the simplex of probabilities. Let w = (w1,w2, . . . ,wn), N = 1, 2, . . . be subsets of W = {w1,w2, . . .} the infinite set of affine contractive maps on X = [0, 1] and let g the set of the moments of any order of
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Denote by M the Markov operator of the N-maps IFS (w, p) and by
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, with associated moment vector of any order hN. The collage distance between the moment vector of
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is a continuous function and attains an absolute minimum value Δ min on ΠN

Stefano M. Iacus, Davide La Torre

In this paper we review some recent results concerning the approximations of distribution functions and measures on [0, 1] based on iterated function systems.
Stefano M. Iacus, Davide La Torre