2 papers
math.ST2022
Testing Heteroskedasticity in High-Dimensional Linear Regression
Akira Shinkyu
We propose a new testing procedure of heteroskedasticity in high-dimensional linear regression, where the number of covariates can be larger than the sample size. Our testing proce…
math.ST2022
Small Tuning Parameter Selection for the Debiased Lasso
Akira Shinkyu, Naoya Sueishi
In this study, we investigate the bias and variance properties of the debiased Lasso in linear regression when the tuning parameter of the node-wise Lasso is selected to be smaller…