A Mixed Autoregressive-Moving Average Exponential Sequence and Point Process (EARMA 1,1)

1975 ◽  
Author(s):  
P. A. Jacobs ◽  
P. A. Lewis
Keyword(s):  
Point Process ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Exponential Sequence

A mixed autoregressive-moving average exponential sequence and point process (EARMA 1,1)

1977 ◽  
Vol 9 (1) ◽  
pp. 87-104 ◽  
Author(s):  
P. A. Jacobs ◽  
P. A. W. Lewis
Keyword(s):  
Linear Combination ◽  
Point Process ◽  
Moving Average ◽  
Random Variables ◽  
Stationary Sequence ◽  
Autoregressive Moving Average ◽  
Marginal Distributions ◽  
Joint Distributions ◽  
Exponential Random Variables ◽  
Exponential Sequence

A stationary sequence of random variables with exponential marginal distributions and the correlation structure of an ARMA (1, 1) process is defined. The process is formed as a random linear combination of i.i.d. exponential random variables and is very simple to generate on a computer. Moments and joint distributions for the sequence are obtained, as well as limiting properties of sums of the random variables and of the point process whose intervals have the EARMA (1, 1) structure.

A mixed autoregressive-moving average exponential sequence and point process (EARMA 1,1)

1977 ◽  
Vol 9 (01) ◽  
pp. 87-104
Author(s):  
P. A. Jacobs ◽  
P. A. W. Lewis
Keyword(s):  
Linear Combination ◽  
Point Process ◽  
Moving Average ◽  
Random Variables ◽  
Stationary Sequence ◽  
Autoregressive Moving Average ◽  
Marginal Distributions ◽  
Joint Distributions ◽  
Exponential Random Variables ◽  
Exponential Sequence

A stationary sequence of random variables with exponential marginal distributions and the correlation structure of an ARMA (1, 1) process is defined. The process is formed as a random linear combination of i.i.d. exponential random variables and is very simple to generate on a computer. Moments and joint distributions for the sequence are obtained, as well as limiting properties of sums of the random variables and of the point process whose intervals have the EARMA (1, 1) structure.

Extending the correlation structure of exponential autoregressive–moving-average processes

1981 ◽  
Vol 18 (1) ◽  
pp. 181-189 ◽  
Author(s):  
Ed McKenzie
Keyword(s):  
Autocorrelation Function ◽  
Moving Average ◽  
Binary Sequence ◽  
Correlation Structure ◽  
Autoregressive Moving Average ◽  
Correlation Structures ◽  
Exponential Random Variables ◽  
Moving Average Processes ◽  
Negative Exponential ◽  
Exponential Sequence

Some recent constructions for the generation of dependent sequences of identically distributed negative exponential random variables with specific correlation structures are generalized. This is achieved by attributing a correlation structure to the binary sequence which controls the generation of the exponentials. The procedure causes the autocorrelation function of the exponential sequence to copy that of the binary sequence and thus be extended to include negative values and other values beyond the usual range.

Extending the correlation structure of exponential autoregressive–moving-average processes

1981 ◽  
Vol 18 (01) ◽  
pp. 181-189 ◽  
Author(s):  
Ed McKenzie
Keyword(s):  
Autocorrelation Function ◽  
Moving Average ◽  
Binary Sequence ◽  
Correlation Structure ◽  
Autoregressive Moving Average ◽  
Correlation Structures ◽  
Exponential Random Variables ◽  
Moving Average Processes ◽  
Negative Exponential ◽  
Exponential Sequence

Some recent constructions for the generation of dependent sequences of identically distributed negative exponential random variables with specific correlation structures are generalized. This is achieved by attributing a correlation structure to the binary sequence which controls the generation of the exponentials. The procedure causes the autocorrelation function of the exponential sequence to copy that of the binary sequence and thus be extended to include negative values and other values beyond the usual range.

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

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