TY - JOUR
T1 - Unbiased estimation of the MSE matrices of improved estimators in linear regression
AU - Wan, Alan T.K.
AU - Chaturvedi, Anoop
AU - Zou, Guohua
PY - 2003/2
Y1 - 2003/2
N2 - Stein-rule and other improved estimators have scarcely been used in empirical work. One major reason is that it is not easy to obtain precision measures for these estimators. In this paper, we derive unbiased estimators for both the mean squared error (MSE) and the scaled MSE matrices of a class of Stein-type estimators. Our derivation provides the basis for measuring the estimators' precision and constructing confidence bands. Comparisons are made between these MSE estimators and the least squares covariance estimator. For illustration, the methodology is applied to data on energy consumption.
AB - Stein-rule and other improved estimators have scarcely been used in empirical work. One major reason is that it is not easy to obtain precision measures for these estimators. In this paper, we derive unbiased estimators for both the mean squared error (MSE) and the scaled MSE matrices of a class of Stein-type estimators. Our derivation provides the basis for measuring the estimators' precision and constructing confidence bands. Comparisons are made between these MSE estimators and the least squares covariance estimator. For illustration, the methodology is applied to data on energy consumption.
UR - http://www.scopus.com/inward/record.url?scp=0037307348&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0037307348&origin=recordpage
U2 - 10.1080/0266476022000023730
DO - 10.1080/0266476022000023730
M3 - RGC 21 - Publication in refereed journal
SN - 0266-4763
VL - 30
SP - 173
EP - 189
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
IS - 2
ER -