On the Strong Law of Large Numbers for Second Order Stationary Processes and Sequences

1974 ◽  
Vol 18 (2) ◽  
pp. 372-375 ◽  
Author(s):  
V. F. Gaposhkin
1991 ◽  
Vol 28 (4) ◽  
pp. 762-770 ◽  
Author(s):  
Ravi Mazumdar ◽  
Raghavan Kannurpatti ◽  
Catherine Rosenberg

This paper extends the rate conservation principle to cadlag processes whose jumps form a non-stationary point process with a time-dependent intensity. It is shown that this is a direct consequence of path integration and the strong law of large numbers for local martingales. When specialized to mean rates a non-stationary version of Miyazawa's result is obtained which is recovered in the stationary case. Some applications of the result are also given.


Author(s):  
Rafael Kawka

Abstract We present limit theorems for locally stationary processes that have a one sided time-varying moving average representation. In particular, we prove a central limit theorem (CLT), a weak and a strong law of large numbers (WLLN, SLLN) and a law of the iterated logarithm (LIL) under mild assumptions using a time-varying Beveridge–Nelson decomposition.


1991 ◽  
Vol 28 (04) ◽  
pp. 762-770 ◽  
Author(s):  
Ravi Mazumdar ◽  
Raghavan Kannurpatti ◽  
Catherine Rosenberg

This paper extends the rate conservation principle to cadlag processes whose jumps form a non-stationary point process with a time-dependent intensity. It is shown that this is a direct consequence of path integration and the strong law of large numbers for local martingales. When specialized to mean rates a non-stationary version of Miyazawa's result is obtained which is recovered in the stationary case. Some applications of the result are also given.


2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Xiaochen Ma ◽  
Qunying Wu

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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