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
If F is a rich family, then for any estimator Fn of F,
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where V ↓ F0 denotes the limit in the net of shrinking neighborhoods (with respect to the variation distance) of F0 and
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The above theorem states that, for any fixed F0, it is impossible to do better that R0(F0) when we try to estimate F0. The empirical distribution function
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is such
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for all n and so it is asymptotically efficient in the sense above mentioned. The result follows from the continuity of Rn in the variation distance topology (see Gill and Levit, 1995). It is almost trivial to show that also the quantile-based IFS estimator is asymptotically efficient in the sense of the minimax theorem, the only condition to impose is on the number of maps
Nn as in the LIL result.
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