3 papers
math.ST2025
An Easily Tunable Approach to Robust and Sparse High-Dimensional Linear Regression
Takeyuki Sasai, Hironori Fujisawa
Sparse linear regression methods such as Lasso require a tuning parameter that depends on the noise variance, which is typically unknown and difficult to estimate in practice. In t…
stat.ML2024
Sparse Linear Regression when Noises and Covariates are Heavy-Tailed and Contaminated by Outliers
Takeyuki Sasai, Hironori Fujisawa
We investigate a problem estimating coefficients of linear regression under sparsity assumption when covariates and noises are sampled from heavy tailed distributions. Additionally…
math.ST2023
Estimation of sparse linear regression coefficients under -subexponential covariates
Takeyuki Sasai
We tackle estimating sparse coefficients in a linear regression when the covariates are sampled from an -subexponential random vector. This vector belongs to a class of distribu…