In observational studies, regression coefficients for categorical regressors are overwhelmingly presented in terms of contrasts with a reference category. For unordered regressors with many categories
We propose an algorithm, semismooth Newton coordinate descent (SNCD), for the elastic-net penalized Huber loss regression and quantile regression in high dimensional settings. Unlike existing coordina
Values of the linear coefficients obtained via ordinary least-squares fits with a correction to heteroskedasticity. Here, is the value of the -statistic and is the two-tail -value for testing the hypo
Parameter estimate (β), standard error (SE), Wald statistics (Wald), degrees of freedom (df), level of significance (p), odds ratio (Exp (β)) and 95% confidence interval of the odds ratio (95% CI Exp