In a misspecified linear regression model with elliptically contoured errors, the exact biases and risks of least squares, restricted least squares, preliminary test and Stein-type estimators of the error variance are derived. Also, we compare the risk performances of the underlying estimators...

Sample weight calibration, also referred to as calibration estimation, is a widely applied technique in the analysis of survey data. This method borrows strength from a set of auxiliary variables and can produce weighted estimates with smaller mean square errors than those estimators that do not...

This paper demonstrates that the randomization-based “Neyman” and constant-effects estimators for the variance of estimated average treatment effects are equivalent to a variant of the White “heteroskedasticity-robust” estimator and the homoskedastic ordinary least squares (OLS)...

In statistical machine learning, the standard measure of accuracy for models is the prediction error, i.e. the expected loss on future examples. When the data distribution is unknown, it cannot be computed but several resampling methods, such as K-fold cross-validation can be used to obtain an...

The regenerative method for estimating steady-state parameters is one of the basic methods in simulation output analysis. This method depends on central limit theorems for regenerative processes and weakly consistent estimates for the variance constants arising in the central limit theorems. A...