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Estimation of the general spatial regression model (SAC) by the maximum likelihood method | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 238، دوره 13، شماره 1، خرداد 2022، صفحه 2947-2957 اصل مقاله (665.74 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.6027 | ||
| نویسندگان | ||
| Wadhah S. Ibrahim* ؛ Nawras Shanshool Mousa | ||
| College of Management and Economics, Dept. Of Statistic, Mustansiriyah University, Baghdad, Iraq. | ||
| تاریخ دریافت: 16 خرداد 1400، تاریخ پذیرش: 02 آذر 1400 | ||
| چکیده | ||
| That there are indicators or statistical transactions that have appeared in a large way in recent times to describe, summarize and analyse spatial data, when a study is done of many phenomena or a disease is studied, whether it is on humans or animals, we need to analyze the spatial data resulting from those phenomena, as it includes observations of the spatial units. For example, countries or provinces ... etc., all of these are linked to certain points or locations. The study uses the maximum likelihood method to estimate the parameters of the General Spatial Model by employing the model to study cancer which shows the relationship between the dependent variable Y represented by the number of patients and the explanatory variables represented ( average age, tumor size, treame, hormone, immunity) in light of the effect of spatial juxtaposition and using Rook neigh boring criteria. One of the most important conclusions reached is the emergence of significant effects of some explanatory variables on the dependent variable Y, and the estimated values of the dependent variable Y are close to the real values of the same variable. | ||
| کلیدواژه‌ها | ||
| The general spatial regression model؛ Spatial contiguity matrix؛ Rook neighboring criteria؛ Maximum Likelihood Method؛ Cancer | ||
| مراجع | ||
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[1] D. I. M. Al-Azzawi and N. A. I. Al-Shammari, Regression Analysis, Methods and Techniques Using SPSS, Minitab, Eviews, House of Books and Documents in Baghdad (2312), 2014. [2] A. A. A. Akkar, Estimation of semi-parametric regression of data spatially Dependent with application, PhD in Statistics, College of Economics and Administration at Baghdad University, 2018. [3] S. A. S. M. Al-Tamimi, A comparative study of some dynamic model estimation methods for (team data) with practical application, PhD University in Statistics, College of Administration and Economics, University, 2016. [4] L. Anselin, Spatial Econometrics: Methods and Models, Kluwer Academic Publishers Dordrecht Google Scholar, Departments of Geography and Economics, University of California, Santa Barbara, 1988. [5] L. Anselin, Spatial Data Analysis With GIS: An Introduction to Appalachian the Social Sciences, National Center for Geographic Information and Analysis, University of California, Santa Barbara, CA 93106, 1992. [6] J. P. Lesage, The Theory and Practice of Spatial Econometrics, Department of Economics University of Toledo, Ohio, 28(11)( 1999 ). [7] J. P. Lesage, Maximum Likelihood Estimation of Spatial Regression Model, University of Toledo, Ohio, (2004). [8] D. B. Omar, Estimating Parameters of Some Spatial Regression Models With Experimental And Applied Study, College of Administration And Economics at Kirkuk University as., 2020. [9] S. A. Rahim, Comparison Between Classical and Spatial Regression Techniques using Fuzzy Logic, A Thesis Submitted to The council of the College of Commerce University of Sulaimani, 2016. | ||
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