CHANGE LANGUAGE | Home > Doc > On fractal distribution function estimation and applications > Theorem 5 (Iacus and La Torre, 2001)

On fractal distribution function estimation and applications


Theoretical background for affine IFS

2.1 Minimization approach

Theorem 1

Theorem 2 (Collage theorem)

Theorem 3 (Forte and Vrscay, 1995)

2.2 Direct approach

Theorem 4 (Iacus and La Torre, 2001).

Theorem 5 (Iacus and La Torre, 2001)

Corollary 6

Theorem 7

2.3 The choice of affine maps

3 Fourier analysis results

Theorem 8 (Forte and Vrscay, 1998)

Theorem 9 (Collage Theorem for FT, (Forte and Vrscay, 1998))

4 Statistical applications

4.1 Asymptotic results for the quantile-based IFS estimator

Theorem 10

Theorem 11

Theorem 12 (Gill and Levit, 1995)

Theorem 13

4.2 Characteristic function and Fourier density estimation

5 Monte Carlo analysis

5.1 Applications to survival analysis

Final remarks


On fractal distribution function estimation and applications

Theorem 5 (Iacus and La Torre, 2001)



such that

for some


is the fixed point of Tp on

and c is the contractivity constant of Tp. Moreover, the function

is convex.

Thus, the following constrained optimization problem:


can always be solved at least numerically. Another way of choosing the form of Tp is the direct approach, that is the following. Choose n = N +1 points on [0, 1], (x1, . . . , xn), and assume that 0 = x1 < x2 < · · · < x n−1< xn = 1. The proposed functional is the following

open full size image

i = 1, . . . , n−1, where u is any member in the space

Notice that TF is a particular case of Tp where


This is a contraction and, at each iteration, TF passes exactly through the points F(xi). It is almost evident that, when n increases the fixed point of the above functional will be "close" to F. For n small, the choice of a good grid of point is critical. So one question arises: how to choose the n points ? One can proceed case by case but as F is a distribution function one can use its properties. We propose the following solution: take n points (u1 = 0, u2, . . . , un = 1) equally spaced [0, 1] and define q i = F-1, i = 1, . . . , n. The points q i are just the quantiles of F. In this way, it is assured that the profile of F is followed as smooth as possible. In fact, if two quantiles qi and q i+1 are relatively distant each other, than F is slowly increasing in the interval (q i, q i+1) and viceversa. This method is more efficient than simply taking equally spaced points on [0, 1]. With this assumption the functional TF reads as

open full size image

This form of the estimator proposes an intuitive (possibily) good candidate for distribution function estimation. Note that we overcome the problem of moment matching as we don't even need the existence of the moments.

Stefano M. Iacus, Davide La Torre

Performance Trading

Home | Mappa | Staff | Disclaimer | Privacy | Supportaci | Contact

Copyright © ed il suo contenuto sono di esclusiva propriet� di DHDwise. E' vietata la riproduzione anche parziale di qualsiasi parte del sito senza autorizzazione compresa la grafica e il layout. Prima della consultazione del sito leggere il disclaimer. Per informazioni consultare la sezione info.