Estimation and inference in the case of competing sets of estimating equations

摘要

When there is uncertainty concerning the appropriate statistical model and correspondingestimators and inference methods, we use the Cressie-Read measure of divergence to define asemiparametric estimator, beta(alpha), that combines plausible estimation problems. This estimation procedure identifies, conditional on the data, an optimal combination of competing estimators for the unknown parameters associated with the alternative plausible structural model specifications. The optimization is handled internally and avoids the tuning parameters usually necessary in problems of this type. To illustrate finite sample performance, an extensive sampling experiment is conducted to demonstrate the adaptive nature of the estimator for an array of data sampling specifications.