In probability theory, especially in mathematical statistics, a location–scale family is a family of 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").
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| - Lage-Skalen-Familie (de)
- Location–scale family (en)
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| - In der Statistik ist eine Lage-Skalen-Familie bzw. Lage- und Skalenfamilie eine Familie von Wahrscheinlichkeitsverteilungen parametrisiert durch einen Lageparameter und einen nichtnegativen Skalenparameter. (de)
- In probability theory, especially in mathematical statistics, a location–scale family is a family of 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"). (en)
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| - In der Statistik ist eine Lage-Skalen-Familie bzw. Lage- und Skalenfamilie eine Familie von Wahrscheinlichkeitsverteilungen parametrisiert durch einen Lageparameter und einen nichtnegativen Skalenparameter. (de)
- In probability theory, especially in mathematical statistics, a location–scale family is a family of 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"). 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 .
* If has a cumulative distribution function , then has a cumulative distribution function .
* If is a discrete random variable with probability mass function , then is a discrete random variable with probability mass function .
* If is a continuous random variable with probability density function , then is a continuous random variable with probability density function . Moreover, if and are two random variables whose distribution functions are members of the family, and assuming existence of the first two moments and has zero mean and unit variance,then can be written as , where and are the mean and standard deviation 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. (en)
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