  Many statistical practices involve choosing between a full model and reduced
models where some coefficients are reduced to zero. Data were used to select a
model with estimated coefficients. Is it possible to do so and still come up
with an estimator always better than the traditional estimator based on the
full model? The James-Stein estimator is such an estimator, having a property
called minimaxity. However, the estimator considers only one reduced model,
namely the origin. Hence it reduces no coefficient estimator to zero or every
coefficient estimator to zero. In many applications including wavelet analysis,
what should be more desirable is to reduce to zero only the estimators smaller
than a threshold, called thresholding in this paper. Is it possible to
construct this kind of estimators which are minimax? In this paper, we
construct such minimax estimators which perform thresholding. We apply our
recommended estimator to the wavelet analysis and show that it performs the
best among the well-known estimators aiming simultaneously at estimation and
model selection. Some of our estimators are also shown to be asymptotically
optimal.
