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In statistics and econometrics, extremum estimators is a wide class of estimators for parametric models that are calculated through maximization (or minimization) of a certain ''objective function'', which depends on the data. The general theory of extremum estimators was developed by . == Definition == An estimator is called an extremum estimator, if there is an ''objective function'' such that : where Θ is the possible range of parameter values. Sometimes a slightly weaker definition is given: : where ''o''''p''(1) is the variable converging in probability to zero. With this modification doesn’t have to be the exact maximizer of the objective function, just be sufficiently close to it. The theory of extremum estimators does not specify what the objective function should be. There are various types of objective functions suitable for different models, and this framework allows us to analyse the theoretical properties of such estimators from a unified perspective. The theory only specifies the properties that the objective function has to possess, and when one selects a particular objective function, he or she only has to verify that those properties are satisfied. 抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)』 ■ウィキペディアで「Extremum estimator」の詳細全文を読む スポンサード リンク
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