A shift parameter estimation based on smoothed Kolmogorov--Smirnov

A new procedure of shift parameter estimation in the two-sample location problem is investigated and compared with existing estimators. The proposed procedure smooths the empirical distribution functions of each random sample and replaces empirical distribution functions in the two-sample Kolmogorov--Smirnov method. The smoothed Kolmogorov--Smirnov is minimized with respect to an arbitrary shift variable in order to find an estimate of the shift parameter. The proposed procedure can be considered the smoothed version of a very little known method of shift parameter estimation from Rao-Schuster-Littell (RSL) [Rao <italic>et al.</italic>, <italic>Estimation of shift and center of symmetry based on Kolmogorov--Smirnov statistics</italic>, Ann. Stat. 3(4) (1975), pp. 862--873]. Their estimator will be discussed and compared with the proposed estimator in this paper. An example and simulation studies have been performed to compare the proposed procedure with existing shift parameter estimators such as Hodges--Lehmann (H--L) and least squares in addition to RSL's estimator. The results show that the proposed estimator has lower mean-squared error as well as higher relative efficiency against RSL's estimator under normal or contaminated normal model assumptions. Moreover, the proposed estimator performs competitively against H--L and least-squares shift estimators. Smoother function and bandwidth selections are also discussed and several alternatives are proposed in the study.