Nonresponse prediction in an establishment survey using combination of statistical learning methods

Nonrespose is a source of error in the survey results and national statistical organizations are always looking for ways to control and reduce it. Predicting nonrespons sampling units in the survey before conducting the survey is one of the solutions that can help a lot in reducing and treating the survey nonresponse. Recent advances in technology and the facilitation of complex calculations have made it possible to apply statistical learning methods, such as regression and classification trees or support vector machines, to many issues, including predicting the nonresponse of sampling units in statistics. In this article, while reviewing the above methods, the nonresponse sampling units are predicted using them in an establishment survey and it is shown that a combination of the above methods is more accurate in predicting the correct nonresponse than any of these methods.