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Ancillary statistic : ウィキペディア英語版
Ancillary statistic

In statistics, an ancillary statistic is a statistic whose sampling distribution does not depend on the parameters of the model. An ancillary statistic is a pivotal quantity that is also a statistic. Ancillary statistics can be used to construct prediction intervals.
This concept was introduced by the statistical geneticist Sir Ronald Fisher.
==Example==

Suppose ''X''1, ..., ''X''''n'' are independent and identically distributed, and are normally distributed with unknown expected value ''μ'' and known variance 1. Let
:\overline_n = \frac
be the sample mean.
The following statistical measures of dispersion of the sample
*Range: max(''X''1, ..., ''X''''n'') − min(''X''1, ..., ''Xn'')
*Interquartile range: ''Q''3 − ''Q''1
*Sample variance:
:: \hat^2:=\,\frac
are all ''ancillary statistics'', because their sampling distributions do not change as ''μ'' changes. Computationally, this is because in the formulas, the ''μ'' terms cancel – adding a constant number to a distribution (and all samples) changes its sample maximum and minimum by the same amount, so it does not change their difference, and likewise for others: these measures of dispersion do not depend on location.
Conversely, given i.i.d. normal variables with known mean 1 and unknown variance ''σ''2, the sample mean \overline is ''not'' an ancillary statistic of the variance, as the sampling distribution of the sample mean is ''N''(1, ''σ''2/''n''), which does depend on ''σ'' 2 – this measure of location (specifically, its standard error) depends on dispersion.

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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