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A well-behaved statistic is a term sometimes used in the theory of statistics to describe part of a procedure. This usage is broadly similar to the use of well-behaved in more general mathematics. It is essentially an assumption about the formulation of an estimation procedure (which entails the specification of an estimator or statistic) that is used to avoid giving extensive details about what conditions need to hold. In particular it means that the statistic is not an unusual one in the context being studied. Due to this, the meaning attributed to ''well-behaved statistic'' may vary from context to context. The present article is mainly concerned with the context of data mining procedures applied to statistical inference and, in particular, to the group of computationally intensive procedure that have been called algorithmic inference. ==Algorithmic inference== (詳細はalgorithmic inference, the property of a statistic that is of most relevance is the pivoting step which allows to transference of probability-considerations from the sample distribution to the distribution of the parameters representing the population distribution in such a way that the conclusion of this statistical inference step is compatible with the sample actually observed. By default, capital letters (such as ''U'', ''X'') will denote random variables and small letters (''u'', ''x'') their corresponding realizations and with gothic letters (such as ) the domain where the variable takes specifications. Facing a sample , given a sampling mechanism , with scalar, for the random variable ''X'', we have : The sampling mechanism , of the statistic ''s'', as a function ? of with specifications in , has an explaining function defined by the master equation: : for suitable seeds and parameter ? 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「Well-behaved statistic」の詳細全文を読む スポンサード リンク
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