A limit distribution theorem for sums of dependent random variables

1959 ◽  
Vol 10 (1-2) ◽  
pp. 125-131 ◽  
Author(s):  
P. Révész
Keyword(s):  
Limit Distribution ◽  
Random Variables ◽  
Dependent Random Variables ◽  
Distribution Theorem

A limit theorem with applications in order statistics

1974 ◽  
Vol 11 (1) ◽  
pp. 219-222 ◽  
Author(s):  
János Galambos
Keyword(s):  
Limit Theorem ◽  
Order Statistics ◽  
Limit Distribution ◽  
Probability Space ◽  
Random Variables ◽  
Analytic Method ◽  
Sufficient Condition ◽  
Dependent Random Variables ◽  
Special Case ◽  
Infinite Sequences

Let A1, A2, ···, An be events on a given probability space and let Br, n be the event that exactly r of the A's occur. Let further Sk (n) be the kth binomial moment of the number of the A's which occur. A sufficient condition is given for the existence of lim P (Br,n), as n→ + ∞, in terms of limits of the Sk(n)'s and a formula is given for the limit above. This formula for the limit is similar to the sieve theorem of Takács (1967) for infinite sequences of events and in the proof we make use of Takács's analytic method. The result is immediately applicable to the limit distribution of the maximum of (dependent) random variables X1, X2, ···, Xn by choosing Aj = {Xj ≧ x}. Our main theorem is reformulated for this special case and an example is given for illustration.

A limit theorem with applications in order statistics

1974 ◽  
Vol 11 (01) ◽  
pp. 219-222
Author(s):  
János Galambos
Keyword(s):  
Limit Theorem ◽  
Order Statistics ◽  
Limit Distribution ◽  
Probability Space ◽  
Random Variables ◽  
Analytic Method ◽  
Sufficient Condition ◽  
Dependent Random Variables ◽  
Special Case ◽  
Infinite Sequences

Let A 1, A 2, ···, An be events on a given probability space and let Br, n be the event that exactly r of the A's occur. Let further Sk (n) be the kth binomial moment of the number of the A's which occur. A sufficient condition is given for the existence of lim P (Br,n ), as n→ + ∞, in terms of limits of the Sk (n)'s and a formula is given for the limit above. This formula for the limit is similar to the sieve theorem of Takács (1967) for infinite sequences of events and in the proof we make use of Takács's analytic method. The result is immediately applicable to the limit distribution of the maximum of (dependent) random variables X 1, X 2, ···, Xn by choosing Aj = {Xj ≧ x}. Our main theorem is reformulated for this special case and an example is given for illustration.

scholarly journals On Some Conditions for Strong Law of Large Numbers for Weighted Sums of END Random Variables under Sublinear Expectations

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Xiaochen Ma ◽  
Qunying Wu
Keyword(s):  
Probability Space ◽  
Law Of Large Numbers ◽  
Random Variables ◽  
Weighted Sums ◽  
Dependent Random Variables ◽  
Strong Law ◽  
Sublinear Expectation ◽  
Negatively Dependent Random Variables ◽  
Large Numbers ◽  
End Random Variables

In this article, we research some conditions for strong law of large numbers (SLLNs) for weighted sums of extended negatively dependent (END) random variables under sublinear expectation space. Our consequences contain the Kolmogorov strong law of large numbers and the Marcinkiewicz strong law of large numbers for weighted sums of extended negatively dependent random variables. Furthermore, our results extend strong law of large numbers for some sequences of random variables from the traditional probability space to the sublinear expectation space context.

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