A Nonparametric Test for the Parallelism of Two First-Order Autoregressive Processes

1999 ◽  
Vol 41 (1) ◽  
pp. 59-65 ◽  
Author(s):  
Jiin-Huarng Guo
Keyword(s):  
Nonparametric Test ◽  
Autoregressive Processes ◽  
First Order

Autoregressive processes in optimization

1988 ◽  
Vol 25 (2) ◽  
pp. 302-312 ◽  
Author(s):  
Tomáš Cipra
Keyword(s):  
Dynamic Structure ◽  
Rates Of Convergence ◽  
Geometric Ergodicity ◽  
Autoregressive Processes ◽  
Stochastic Linear Programming ◽  
Vector Autoregressive ◽  
First Order ◽  
Convergence Of Moments ◽  
Geometric Convergence ◽  
Vector Autoregressive Processes

Vector autoregressive processes of the first order are considered which are non-negative and optimize a linear objective function. These processes may be used in stochastic linear programming with a dynamic structure. By using Tweedie's results from the theory of Markov chains, conditions for geometric rates of convergence to stationarity (i.e. so-called geometric ergodicity) and for existence and geometric convergence of moments of these processes are obtained.

Non-strong mixing autoregressive processes

1984 ◽  
Vol 21 (4) ◽  
pp. 930-934 ◽  
Author(s):  
Donald W. K. Andrews
Keyword(s):  
Direct Proof ◽  
Strong Mixing ◽  
Autoregressive Processes ◽  
Mixing Conditions ◽  
First Order ◽  
Insight Into

Certain first-order autoregressive processes are shown not to be strong mixing. A direct proof is given. This proof gives considerably more insight into the nature of the result than do proofs by contradiction. The result and proof help to clarify the relation between the autoregressive and strong mixing conditions.

Autoregressive processes in optimization

1988 ◽  
Vol 25 (02) ◽  
pp. 302-312
Author(s):  
Tomáš Cipra
Keyword(s):  
Dynamic Structure ◽  
Rates Of Convergence ◽  
Geometric Ergodicity ◽  
Autoregressive Processes ◽  
Stochastic Linear Programming ◽  
Vector Autoregressive ◽  
First Order ◽  
Convergence Of Moments ◽  
Geometric Convergence ◽  
Vector Autoregressive Processes

Vector autoregressive processes of the first order are considered which are non-negative and optimize a linear objective function. These processes may be used in stochastic linear programming with a dynamic structure. By using Tweedie's results from the theory of Markov chains, conditions for geometric rates of convergence to stationarity (i.e. so-called geometric ergodicity) and for existence and geometric convergence of moments of these processes are obtained.

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