On the Dependence of the Convergence Rate in the Strong Law of Large Numbers for Stationary Processes on the Rate of Decay of the Correlation Function

1982 ◽  
Vol 26 (4) ◽  
pp. 706-720 ◽  
Author(s):  
V. F. Gaposhkin
Keyword(s):  
Correlation Function ◽  
Convergence Rate ◽  
Law Of Large Numbers ◽  
Stationary Processes ◽  
Strong Law ◽  
Large Numbers ◽  
Rate Of Decay

scholarly journals The strong law of large numbers for dependent vector processes with decreasing correlation: “Double averaging concept”

2001 ◽  
Vol 7 (1) ◽  
pp. 87-95 ◽  
Author(s):  
Alex S. Poznyak
Keyword(s):  
Correlation Function ◽  
Law Of Large Numbers ◽  
Random Processes ◽  
Strong Law ◽  
Large Numbers ◽  
Impulse Function ◽  
Double Averaging ◽  
Dependent Vector ◽  
Vector Sequences ◽  
New Form

A new form of the strong law of large numbers for dependent vector sequences using the “double averaged” correlation function is presented. The suggested theorem generalizes the well-known Cramer–Lidbetter's theorem and states more general conditions for fulfilling the strong law of large numbers within the class of vector random processes generated by a non stationary stable forming filters with an absolutely integrable impulse function.

Exact convergence rate of an Erdös-Rényi strong law for moving quantiles

1986 ◽  
Vol 23 (02) ◽  
pp. 355-369
Author(s):  
Paul Deheuvels ◽  
Josef Steinebach
Keyword(s):  
Convergence Rate ◽  
Order Statistic ◽  
Law Of Large Numbers ◽  
Random Variables ◽  
Monotonicity Condition ◽  
Strict Monotonicity ◽  
Strong Law ◽  
Large Numbers ◽  
Image Position

Consider a sequence U 1, U 2 , · ·· of i.i.d. uniform (0, 1)-random variables. For fixed α ∈ (0, 1), let U(n, K) denote the [Kα]th order statistic of the subsample Un +1, · ··, Un +K , and set . Book and Truax (1976) proved the following analogue of the Erdös-Rényi (1970) strong law of large numbers: for α < u < 1 and C = C(α, u) such that −1/C = αlog(u/α)+ (1 – α)log((l – u)/(1 –α)), it holds almost surely that In view of the Deheuvels–Devroye (1983) improvements of the original Erdös-Rényi law, we determine the lim inf and lim sup of where K = [C log N]. This improves (∗), showing that it holds with a best-possible convergence rate of order O(log log N/log N). Using the quantile transformation the result can be extended to a general i.i.d. sequence X 1, X 2, · ·· with d.f. F satisfying a strict monotonicity condition.

Exact convergence rate of an Erdös-Rényi strong law for moving quantiles

1986 ◽  
Vol 23 (2) ◽  
pp. 355-369 ◽  
Author(s):  
Paul Deheuvels ◽  
Josef Steinebach
Keyword(s):  
Convergence Rate ◽  
Order Statistic ◽  
Law Of Large Numbers ◽  
Random Variables ◽  
Monotonicity Condition ◽  
Strict Monotonicity ◽  
Strong Law ◽  
Large Numbers ◽  
Image Position

Consider a sequence U1, U2, · ·· of i.i.d. uniform (0, 1)-random variables. For fixed α ∈ (0, 1), let U(n, K) denote the [Kα]th order statistic of the subsample Un+1, · ··, Un+K, and set . Book and Truax (1976) proved the following analogue of the Erdös-Rényi (1970) strong law of large numbers: for α < u < 1 and C = C(α, u) such that −1/C = αlog(u/α)+ (1 – α)log((l – u)/(1 –α)), it holds almost surely that In view of the Deheuvels–Devroye (1983) improvements of the original Erdös-Rényi law, we determine the lim inf and lim sup of where K = [C log N]. This improves (∗), showing that it holds with a best-possible convergence rate of order O(log log N/log N). Using the quantile transformation the result can be extended to a general i.i.d. sequence X1, X2, · ·· with d.f. F satisfying a strict monotonicity condition.

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