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These classes are relatively broad, as they include extremum estimation with smooth or nonsmooth objective functions; also, the rate of convergence to the asymptotic distribution is not required to be<jats:italic>n<\/jats:italic><jats:sup>\u2212\u00bd<\/jats:sup>. We present examples illustrating the content of our result. In particular, we apply our result to a class of weighted Huber estimators, and obtain, among other things, analogs of the generalized least-squares estimator for least<jats:italic>L<\/jats:italic><jats:sub><jats:italic>p<\/jats:italic><\/jats:sub>-estimation, 1 \u2264<jats:italic>p<\/jats:italic>&lt; \u221e.<\/jats:p>","DOI":"10.1017\/s026646660000801x","type":"journal-article","created":{"date-parts":[[2009,3,10]],"date-time":"2009-03-10T15:46:44Z","timestamp":1236700004000},"page":"633-648","source":"Crossref","is-referenced-by-count":17,"title":["Determination of Estimators with Minimum Asymptotic Covariance Matrices"],"prefix":"10.1017","volume":"9","author":[{"given":"Charles 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