Step function

Summary

In mathematics, a function on the real numbers is called a step function if it can be written as a finite linear combination of indicator functions of intervals. Informally speaking, a step function is a piecewise constant function having only finitely many pieces. Definition and first consequences A function f\colon \mathbb{R} \rightarrow \mathbb{R} is called a step function if it can be written as :f(x) = \sum\limits_{i=0}^n \alpha_i \chi_{A_i}(x), for all real numbers x where n\ge 0, \alpha_i are real numbers, A_i are intervals, and \chi_A is the indicator function of A: :\chi_A(x) = \begin{cases} 1 & \text{if } x \in A \ 0 & \text{if } x \notin A \ \end{cases} In this definition, the intervals A_i can be assumed to have the following two properties:

The intervals are pairwise disjoint: A_i \cap A_j = \emptyset for

Official source

https://en.wikipedia.org/wiki/Step_function

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Let X = {X(t); t ∈ RN} be a (N,d) fractional Brownian motion in Rd of index H ∈ (0,1). We study the local time of X for all temporal dimensions N and spatial dimensions d for which local time exist. We obtain two main results : R1. If we denote by Lx(I) the local time of X at x on I ⊂ RN, then there exists a positive finite constant c such that mφ(X-1(0) ∩ [0,1]N) = c L0([0,1]N), where φ(r) = rN-dH (log log 1/r)dH/N and mφ(E) is the Hausdorff φ-measure of E. This solves the problem of the relationship between the local time and the exact Hausdorff measure of zero set for X. R2. We refine results of Xiao (1997) for the local times of (N,d) fractional Brownian motion. We prove the law of iterated logarithm and global Hölder condition for the local time for our process. These results establish interesting properties which were only partially proved in the literature. This literature began with the work of Taylor and Wendel (1966) and Perkins (1981) for the first result; Kesten (1965) and Perkins (1981) for the second on the Brownian motion. It continued with several works of which that of Xiao (1997) on the locally nondeterministic processes with stationnary increments including the (N,d) fractional Brownian motion. An intermediate result is found to solve the case N > 1. We generalize the result of Kasahara et al. (1999) on the tail probability of local time.

EPFL2008

The Exact Hausdorff Measure of the Zero Set of Fractional Brownian Motion

Driss Baraka, Thomas Mountford

Let {X(t), t is an element of R-N} be a fractional Brownian motion in R-d of index H. If L(0,I) is the local time of X at 0 on the interval I subset of R-N, then there exists a positive finite constant c(=c(N,d,H)) such that

Springer US2011

Nonstationary Positive Definite Tapering On The Plane

Raphaël Huser

A common problem in spatial statistics is to predict a random field f at some spatial location t(0) using observations f(t(1)),..., f(t(n)) at t(1),..., t(n) epsilon IRd. Recent work by Kaufman et al. and Furrer et al. studies the use of tapering for reducing the computational burden associated with likelihood-based estimation and prediction in large spatial datasets. Unfortunately, highly irregular observation locations can present problems for stationary tapers. In particular, there can exist local neighborhoods with too few observations for sufficient accuracy, while others have too many for computational tractability. In this article, we show how to generate nonstationaty covariance tapers T(s, t) such that the number of observations in {t : T(s, t) > 0} is approximately a constant function of s. This ensures that tapering neighborhoods do not have too many points to cause computational problems but simultaneously have enough local points for accurate prediction. We focus specifically on tapering in two dimensions where quasi-conformal theory can be used. Supplementary materials for the article are available online.

American Statistical Association2013