Speed of Convergence of the Distribution of the Maximum of Sums of Independent Random Variables to a Limit Distribution

1966 ◽  
Vol 11 (3) ◽  
pp. 438-441 ◽  
Author(s):  
B. A. Rogozin
Keyword(s):  
Limit Distribution ◽  
Random Variables ◽  
Independent Random Variables ◽  
Speed Of Convergence ◽  
Distribution Of The Maximum

scholarly journals The Class of Distributions Associated with the Generalized Pollaczek-Khinchine Formula

2012 ◽  
Vol 49 (3) ◽  
pp. 883-887 ◽  
Author(s):  
Offer Kella
Keyword(s):  
Brownian Motion ◽  
Stationary Distribution ◽  
Lévy Process ◽  
Random Variables ◽  
Levy Process ◽  
Independent Random Variables ◽  
Reflected Brownian Motion ◽  
Distribution Of The Maximum ◽  
Workload Distribution ◽  
Stationary Workload

The goal is to identify the class of distributions to which the distribution of the maximum of a Lévy process with no negative jumps and negative mean (equivalently, the stationary distribution of the reflected process) belongs. An explicit new distributional identity is obtained for the case where the Lévy process is an independent sum of a Brownian motion and a general subordinator (nondecreasing Lévy process) in terms of a geometrically distributed sum of independent random variables. This generalizes both the distributional form of the standard Pollaczek-Khinchine formula for the stationary workload distribution in the M/G/1 queue and the exponential stationary distribution of a reflected Brownian motion.

The Distribution of the Maximum of Partial Sums of Independent Random Variables

1950 ◽  
Vol 2 ◽  
pp. 375-384 ◽  
Author(s):  
Mark Kac ◽  
Harry Pollard
Keyword(s):  
Random Variables ◽  
Independent Random Variables ◽  
Partial Sums ◽  
Distribution Of The Maximum

1. The problem. It has been shown [1] that if Xi, + X2, … are independent random variables each of density then(1.1)

On a Problem of Fluctuations of Sums of Independent Random Variables

1975 ◽  
Vol 12 (S1) ◽  
pp. 29-37
Author(s):  
Lajos Takács
Keyword(s):  
Limit Distribution ◽  
Random Variables ◽  
Independent Random Variables ◽  
Partial Sums ◽  
Discrete Random Variables ◽  
Mutually Independent ◽  
Independent And Identically Distributed

The author determines the distribution and the limit distribution of the number of partial sums greater than k (k = 0, 1, 2, …) for n mutually independent and identically distributed discrete random variables taking on the integers 1, 0, − 1, − 2, ….

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