作者：Bassett, Robert; Deride, Julio
作者单位：United States Department of Defense; United States Navy; Naval Postgraduate School; Universidad Tecnica Federico Santa Maria
摘要：We study statistical estimators computed using iterative optimization methods that are not run until completion. Classical results on maximum likelihood estimators (MLEs) assert that a one-step estimator (OSE), in which a single Newton-Raphson iteration is performed from a starting point with certain properties, is asymptotically equivalent to the MLE. We further develop these early-stopping results by deriving properties of one-step estimators defined by a single iteration of scaled proximal ...
