After performing a randomized experiment, researchers often use ordinary least-square (OLS) regression to adjust for baseline covariates when estimating the average treatment effect. It is widely known that resulting confidence interval valid even if linear model misspecified. In this article, we generalize conclusion covariate adjustment with nonlinear models. We introduce an intuitive way any...

The Kaplan–Meier estimator is a nonparametric estimator which may be used to estimate the survival distribution function from censored data. The estimator may be obtained as the limiting case of the classical actuarial (life table) estimator, and it seems to have been first proposed by Böhmer [2]. It was, however, lost sight of by later researchers and not investigated further until the importa...

The problem of estimation in multivariate linear calibration with multivariate response and explanatory variables is considered. In this calibration problem two estimators are well-known; one is the classical estimator and the other is the inverse estimator. In this paper we show that the inverse estimator is a proper Bayes estimator under the quadratic loss with respect to a prior distribution...

With a focus on linear models with smooth functional covariates, we propose penalization framework (SACR) based the nonzero centered ridge, where center of penalty is adaptively reweighted, starting from ordinary ridge solution as initial center. In particular, introduce convex formulation that jointly estimates model’s coefficients and weight function, roughness center, constraints weights in ...