scholarly journals EXPONENTIAL PROBABILITY INEQUALITY FOR LINEARLY NEGATIVE QUADRANT DEPENDENT RANDOM VARIABLES

2007 ◽  
Vol 22 (1) ◽  
pp. 137-143 ◽  
Author(s):  
Mi-Hwa Ko ◽  
Yong-Kab Choi ◽  
Yue-Soon Choi
Keyword(s):  
Random Variables ◽  
Probability Inequality ◽  
Dependent Random Variables ◽  
Exponential Probability

NEW EXPONENTIAL PROBABILITY INEQUALITY AND COMPLETE CONVERGENCE FOR F-LNQD RANDOM VARIABLES SEQUENCE WITH APPLICATION TO AR(1)MODEL GENERATED BY F-LNQD ERRORS

2021 ◽  
Vol 21 (2) ◽  
pp. 437-448
Author(s):  
NADJIA AZZEDINE ◽  
AMINA ZEBLAH ◽  
SAMIR BENAISSA
Keyword(s):  
Complete Convergence ◽  
Random Variables ◽  
Tail Probability ◽  
Probability Inequality ◽  
Autoregressive Processes ◽  
Probability Inequalities ◽  
First Order ◽  
Probability And Statistics ◽  
Exponential Probability

The exponential probability inequalities have been important tools in probability and statistics. In this paper, we prove a new tail probability inequality for the distri-butions of sums of conditionally linearly negative quadrant dependent (F-LNQD , in short) random variables, and obtain a result dealing with conditionally complete con-vergence of first-order autoregressive processes with identically distributed (F-LNQD) innovations.

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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