Subset selection of double-threshold moving average models through the application of the Bayesian method

2022 ◽  
Vol 15 (1) ◽  
pp. 51-61
Jinshan Liu ◽  
Jiazhu Pan ◽  
Qiang Xia ◽  
Ying Xiao
1970 ◽  
Vol 92 (3) ◽  
pp. 667-676 ◽  
S. J. Deutsch ◽  
S. M. Wu

Autoregressive-moving average models are developed to represent grinding wheel profiles for different combinations of sampling parameters including the sample interval, the number of observations, and the length of record. Using 46 and 120 grit grinding wheels, the effects of the choice of sample interval and number of observations on the appropriate model form are investigated. Discrimination between models for different grit size grinding wheels is discussed. A new criterion is proposed for the selection of the sample interval, based on observations per grit (OPG), to achieve comparable discrete approximations of the wheels and to maximize discrimination between models of different wheels.

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed

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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