Seminar Notice

R Seminar Notice


       JACKKNIFING THE MEAN SQUARED ERROR OF EMPIRICAL BEST PREDICTOR

	            Partha Lahiri
          Department of Mathematics and Statistics
	   University of Nebraska-Lincoln	          


A jackknife estimator of the mean squared error (MSE) of 
empirical best predictor (EBP) is proposed for a general
model which includes mixed linear models and mixed
logistic models as special cases. Asymptotic unbiasedness
of the proposed jackknife estimator is proved under certain mild
regularity conditions. The asymptotic results are valid
for a fairly general class of M-estimators of the model parameters.
For the special case of mixed linear model, the method
only requires posterior linearity assumption 
in order to obtain the jackknife MSE estimator
of EBLUP (which is also EBP) of a mixed effect. Thus the
proposed jackknife method is more robust than the existing
methods which are mostly based on the assumption of normality.