Advanced Sparse Estimation Methods: With a Focus on Missing Data Analysis and Transfer Learning

Abstract

Sparse estimation is widely used in data science as a parameter estimation method for high-dimensional data. However, in real-world data and problems, Lasso and other basic methods may not provide sufficient accuracy, computational efficiency, and stability. In this paper, we introduce recent developments in sparse estimation methods for real-world complex and difficult problems, with a particular focus on missing data analysis and transfer learning.