A Poisson approximation with applications to the number of maxima in a discrete sample
โ Scribed by Peter Olofsson
- Publisher
- Elsevier Science
- Year
- 1999
- Tongue
- English
- Weight
- 80 KB
- Volume
- 44
- Category
- Article
- ISSN
- 0167-7152
No coin nor oath required. For personal study only.
โฆ Synopsis
We present a Poisson approximation with applications to extreme value theory. Let X1; X2; : : : be i.i.d. and let
n be the j largest order statistics. Then the asymptotic behavior of the vector (M (1) n ; : : : ; M ( j) n ) is the same as that of (M (1) N ; : : : ; M ( j) N ) where N is a random variable which is independent of X1; X2; : : : and has a Poisson distribution with mean n. The distribution of (M (1) N ; : : : ; M ( j) N ) is easy to obtain since the points X1; X2; : : : ; XN form a Poisson process on the real line. The mean measure is n dF where F is the distribution function of the Xi. We apply this to the problem of multiple maxima in discrete samples, in particular from the geometric distribution where it is known that the number of maxima has no limiting distribution.
๐ SIMILAR VOLUMES
Consider {Xj, j>~ 1 }, a sequence of i.i.d., positive, integer-valued random variables. Let K, denote the number of the integer j ~ {1,2,... ,n} for which Xj = maxl~m~~n)=0 and prove that Kn converges almost surely to one, if and only if ~t(P(Xl = n)/P(X1 >~n)) 2 < c~. Some of the results were shown
## Introduction. The theory of discrete approximation serves as a framework of approximation and discretization methods for the numerical solution of functional equations. This theory allows a unified functional-analytic treatment of these methods. It was developed by several authors (see e.g. the
we show how local approximations, each accurate on a subinterval, can be blended together to form a global approximation which is accurate over the entire interval. The blending functions are smoothed approximations to a step function, constructed using the error function. The local approximations m