First-order spatial dependent count integer-valued autoregressive (Sp-DCINAR(1,1)) process

In this article, we propose a new model to model the spatial count data on a two-dimensional regular grid using binomial thinning operator with dependent Bernoulli counting series. The model is called “first-order spatial dependent count integer-valued autoregressive (Sp-DCINAR(1,1)) model.” Some of its properties have been derived and the estimation of the parameters of the model are obtained by the Yule-Walker method, conditional least squares method and conditional maximum likelihood method. Finally, numerical results are presented together with an application to a two-dimensional practical data set.</p

Predicted values and intervals for the recommended dosage

<p>Predicted values and intervals for the recommended dosage</p

Box plot of the data set

<p>Box plot of the data set</p

OLS fitted models

<p>OLS fitted models</p

Scatter plot of hexaconazole concentration for the double recommended dosage

<p>Scatter plot of hexaconazole concentration for the double recommended dosage</p

Comparison of fitted lines

<p>Comparison of fitted lines</p

Fitted line with observed value of concentration

<p>Fitted line with observed value of concentration</p

Fitted line and predicted intervals

<p>Fitted line and predicted intervals</p

Daily precipitation and temperature

<p>Daily precipitation and temperature</p

Diagnostics plot of the fitted regression model

<p>Diagnostics plot of the fitted regression model</p