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The regressions of least absolute shrinkage and selection operator with applications | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 139، دوره 13، شماره 2، مهر 2022، صفحه 1735-1746 اصل مقاله (923.33 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.6553 | ||
| نویسنده | ||
| Aseel Noori Saleh* | ||
| Department of Studies, Planning and Follow-up, Ministry of Higher Education and Scientific Research, Iraq | ||
| تاریخ دریافت: 15 دی 1400، تاریخ بازنگری: 11 اسفند 1400، تاریخ پذیرش: 27 اسفند 1400 | ||
| چکیده | ||
| Lasso regression model is a causal model based on providing a more accurate estimator through the model's dependence on shrinkage, in which the data values were reduced towards the data center. This model solves the problems of multicollinearity presence of high relationships between the explanatory variables of the model. In this research, a number of factors (sample size, number of explanatory variables and pollution rate) were adopted in order to observe the ability of these factors to effect Lasso regression. | ||
| کلیدواژه‌ها | ||
| LASSO regressions؛ Mean Square Error؛ multicollinearity | ||
| مراجع | ||
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