scholarly journals Approximation of solutions of multi-dimensional linear stochastic differential equations defined by weakly dependent random variables

2017 ◽  
Vol 2 (3) ◽  
pp. 377-384
Author(s):  
Hiroshi Takahashi ◽  
◽  
Ken-ichi Yoshihara ◽  
Keyword(s):  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Random Variables ◽  
Dependent Random Variables ◽  
Weakly Dependent Random Variables ◽  
Weakly Dependent

Limit theorems for weakly dependent random variables

1981 ◽  
Vol 20 (3) ◽  
pp. 244-249 ◽  
Author(s):  
R. Lapinskas
Keyword(s):  
Limit Theorems ◽  
Random Variables ◽  
Dependent Random Variables ◽  
Weakly Dependent Random Variables ◽  
Weakly Dependent

Moment bounds for non-stationary dependent sequences

1994 ◽  
Vol 31 (3) ◽  
pp. 731-742 ◽  
Author(s):  
Tae Yoon Kim
Keyword(s):  
Random Variables ◽  
Partial Sums ◽  
Unified Approach ◽  
Dependent Random Variables ◽  
Combinatorial Argument ◽  
Stationarity Condition ◽  
Weakly Dependent Random Variables ◽  
Mixing Sequences ◽  
Moment Bounds ◽  
Weakly Dependent

We provide a unified approach for establishing even-moment bounds for partial sums for a class of weakly dependent random variables satisfying a stationarity condition. As applications, we discuss moment bounds for various types of mixing sequences. To obtain even-moment bounds, we use a ‘combinatorial argument' developed by Cox and Kim (1990).

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