scholarly journals Empirical distribution functions and functions of order statistics for mixing random variables

1980 ◽  
Vol 10 (3) ◽  
pp. 405-425 ◽  
Author(s):  
Madan L. Puri ◽  
Lanh T. Tran
Keyword(s):  
Order Statistics ◽  
Empirical Distribution ◽  
Random Variables ◽  
Distribution Functions ◽  
Mixing Random Variables ◽  
Empirical Distribution Functions

scholarly journals Limiting behavior of the perturbed empirical distribution functions evaluated at U-statistics for strongly mixing sequences of random variables

1997 ◽  
Vol 10 (1) ◽  
pp. 3-20 ◽  
Author(s):  
Shan Sun ◽  
Ching-Yuan Chiang
Keyword(s):  
Empirical Distribution Function ◽  
Empirical Distribution ◽  
Random Variables ◽  
Distribution Functions ◽  
Limiting Behavior ◽  
U Statistics ◽  
Strongly Mixing ◽  
Almost Sure Representation ◽  
Mixing Sequences ◽  
Empirical Distribution Functions

We prove the almost sure representation, a law of the iterated logarithm and an invariance principle for the statistic Fˆn(Un) for a class of strongly mixing sequences of random variables {Xi,i≥1}. Stationarity is not assumed. Here Fˆn is the perturbed empirical distribution function and Un is a U-statistic based on X1,…,Xn.

scholarly journals On a probability problem connected with Railway traffic

1991 ◽  
Vol 4 (1) ◽  
pp. 1-27 ◽  
Author(s):  
Lajos Takács
Keyword(s):  
Distribution Function ◽  
Asymptotic Behavior ◽  
Empirical Distribution ◽  
Continuous Distribution ◽  
Random Variables ◽  
Distribution Functions ◽  
Continuous Distribution Function ◽  
Independent Random Variables ◽  
Railway Traffic ◽  
Empirical Distribution Functions

Let Fn(x) and Gn(x) be the empirical distribution functions of two independent samples, each of size n, in the case where the elements of the samples are independent random variables, each having the same continuous distribution function V(x) over the interval (0,1). Define a statistic θn by θn/n=∫01[Fn(x)−Gn(x)]dV(x)−min0≤x≤1[Fn(x)−Gn(x)]. In this paper the limits of E{(θn/2n)r}(r=0,1,2,…) and P{θn/2n≤x} are determined for n→∞. The problem of finding the asymptotic behavior of the moments and the distribution of θn as n→∞ has arisen in a study of the fluctuations of the inventory of locomotives in a randomly chosen railway depot.

scholarly journals Laws of large numbers for L-statistics

1994 ◽  
Vol 7 (2) ◽  
pp. 125-143
Author(s):  
Rimas Norvaiša
Keyword(s):  
Order Statistics ◽  
Necessary Conditions ◽  
Empirical Distribution ◽  
Function Spaces ◽  
Distribution Functions ◽  
Banach Function Spaces ◽  
Laws Of Large Numbers ◽  
Large Numbers ◽  
Empirical Distribution Functions

Consider Ln=n−1∑1≤i≤ncnig(Xn:i) for order statistics Xn:i and let cni=n∫(i−1)/ni/nJdλ for some (Lebesgue) λ-summable over (0,1) function J. Sufficient as well as necessary conditions for limnLn=∫01Jgdλ to hold almost surely and in probability are given. Superposition (or Nemytskii) operators have been used to derive the laws of large numbers for L-statistics from the laws of large numbers in quasi-Banach function spaces for the empirical distribution functions based on X1,…,Xn.

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