Selection in Small Area Estimation under AR-GARCH Models Based on the Gradient Boosting Algorithm

Message:
Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:

The boosting algorithm is a hybrid algorithm to reduce variance, a family of machine learning algorithms in supervised learning. This algorithm is a method to transform weak learning systems into strong systems based on the combination of different results. In this paper, mixture models with random effects are considered for small areas, where the errors follow the AR-GARCH model. To select the variable, machine learning algorithms, such as boosting algorithms, have been proposed. Using simulated and tax liability data, the boosting algorithm's performance is studied and compared with classical variable selection methods, such as the step-by-step method.

Language:
Persian
Published:
Journal of Statistical Sciences, Volume:19 Issue: 1, Spring-Summer 2025
Pages:
1 to 28
https://www.magiran.com/p2856982  
سامانه نویسندگان
  • Sedigheh Zamani Mehreyan
    Author (3)
    (1395) دکتری آمار، دانشگاه رازی کرمانشاه
    Zamani Mehreyan، Sedigheh
اطلاعات نویسنده(گان) توسط ایشان ثبت و تکمیل شده‌است. برای مشاهده مشخصات و فهرست همه مطالب، صفحه رزومه را ببینید.
مقالات دیگری از این نویسنده (گان)