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Location-scale family
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Location-scale family : ウィキペディア英語版
Location-scale family
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 X whose probability distribution function belongs to such a family, the distribution function of Y \stackrel a + b X also belongs to the family (where \stackrel means "equal in distribution"—that is, "has the same distribution as"). Moreover, if X and Y are two random variables whose distribution functions are members of the family, and X has zero mean and unit variance, then Y can be written as Y \stackrel \mu_Y + \sigma_Y X , where \mu_Y and \sigma_Y are the mean and standard deviation of Y .
In other words, a class \Omega of probability distributions is a location-scale family if for all cumulative distribution functions F \in \Omega and any real numbers a \in \mathbb and b > 0 , the distribution function G(x) = F(a + b x) is also a member of \Omega .
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)
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