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In probability theory, especially in mathematical statistics, a location-scale family is a family of univariate probability distributions parametrized by a location parameter and a non-negative scale parameter. For any random variable whose probability distribution function belongs to such a family, the distribution function of also belongs to the family (where means "equal in distribution"—that is, "has the same distribution as"). Moreover, if and are two random variables whose distribution functions are members of the family, and has zero mean and unit variance, then can be written as , where and are the mean and standard deviation of . In other words, a class of probability distributions is a location-scale family if for all cumulative distribution functions and any real numbers and , the distribution function is also a member of . In decision theory, if all alternative distributions available to a decision-maker are in the same location-scale family, and the first two moments are finite, then a two-moment decision model can apply, and decision-making can be framed in terms of the means and the variances of the distributions. == Examples == Often, location-scale families are restricted to those where all members have the same functional form. Well-known families in which the functional form of the distribution is consistent throughout the family include the following: * Normal distribution * Elliptical distribution * Cauchy distribution * Uniform distribution (continuous) * Uniform distribution (discrete) * Logistic distribution * Laplace distribution * Student's t-distribution * Generalized extreme value distribution 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「Location-scale family」の詳細全文を読む スポンサード リンク
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