Estimation of sparse linear regression coefficients under $L$-subexponential covariates

We tackle estimating sparse coefficients in a linear regression when the covariates are sampled from an $L$-subexponential random vector. This vector belongs to a class of distributions that exhibit heavier tails than Gaussian random vector. Previous studies have established error bounds similar to those derived for Gaussian random vectors. However, these methods require stronger conditions than those used for Gaussian random vectors to derive the error bounds. In this study, we present an error bound identical to the one obtained …

class error linear linear regression math.st random regression stat.ml stat.th studies vector vectors