Bivariate Exponential and Geometric Autoregressive and Autoregressive Moving Average Models.

1986 ◽  
Author(s):  
H. W. Block ◽  
N. A. Langberg ◽  
D. S. Stoffer
Keyword(s):  
Moving Average ◽  
Autoregressive Moving Average ◽  
Bivariate Exponential ◽  
Moving Average Models ◽  
Autoregressive Moving Average Models

Bivariate exponential and geometric autoregressive and autoregressive moving average models

1988 ◽  
Vol 20 (4) ◽  
pp. 798-821 ◽  
Author(s):  
H. W. Block ◽  
N. A. Langberg ◽  
D. S. Stoffer
Keyword(s):  
Moving Average ◽  
Real Data ◽  
Autoregressive Moving Average ◽  
Positive Dependence ◽  
Data Set ◽  
Associated Random Variables ◽  
Bivariate Exponential ◽  
Moving Average Models ◽  
Special Cases ◽  
Autoregressive Moving Average Models

We present autoregressive (AR) and autoregressive moving average (ARMA) processes with bivariate exponential (BE) and bivariate geometric (BG) distributions. The theory of positive dependence is used to show that in various cases, the BEAR, BGAR, BEARMA, and BGARMA models consist of associated random variables. We discuss special cases of the BEAR and BGAR processes in which the bivariate processes are stationary and have well-known bivariate exponential and geometric distributions. Finally, we fit a BEAR model to a real data set.

Bivariate exponential and geometric autoregressive and autoregressive moving average models

1988 ◽  
Vol 20 (04) ◽  
pp. 798-821
Author(s):  
H. W. Block ◽  
N. A. Langberg ◽  
D. S. Stoffer
Keyword(s):  
Moving Average ◽  
Real Data ◽  
Autoregressive Moving Average ◽  
Positive Dependence ◽  
Data Set ◽  
Associated Random Variables ◽  
Bivariate Exponential ◽  
Moving Average Models ◽  
Special Cases ◽  
Autoregressive Moving Average Models

We present autoregressive (AR) and autoregressive moving average (ARMA) processes with bivariate exponential (BE) and bivariate geometric (BG) distributions. The theory of positive dependence is used to show that in various cases, the BEAR, BGAR, BEARMA, and BGARMA models consist of associated random variables. We discuss special cases of the BEAR and BGAR processes in which the bivariate processes are stationary and have well-known bivariate exponential and geometric distributions. Finally, we fit a BEAR model to a real data set.

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