June 16, 2024
Hamid Karamikabir

Hamid Karamikabir

Academic Rank: Assistant professor
Address: Department of Statistics, Persian Gulf University, Bushehr, Iran.
Degree: Ph.D in Statistics
Phone: 09188175368
Faculty: Faculty of Intelligent Systems and Data Science

Research

Title
Semi parametric tress shrinkage regression
Type Presentation
Keywords
Semi-parametric regression, Lasso regression, Regression tree, Penalized regression, Non-parametric regression
Researchers Mohamadreza Khalvati Fahliayni (First researcher) , Hamid Karamikabir (Second researcher)

Abstract

Semi-parametric regression models combine elements of parametric and non-parametric regression approaches. Lasso regression, a penalized parametric regression technique, introduces a penalty term to the regression equation to enhance model performance. Regression tree models, a non-parametric approach, partition the data into subsets and build separate models for each subset. By combining these techniques, semi-parametric regression models can capture both linear and non-linear relationships in the data, providing a powerful tool for addressing complex regression problems.