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where n is the sample size.
Let n represent the sample size and X(i) represent
the ith order statistic. Then, when n is odd, the STDMED function
calculates
, where
![{Var}(X_{((n+1)/2)}) =
\frac{1}{B( \frac{n+1}2,\frac{n+1}2 ) }
\displaystyle \...
...-\infty}^\infty
x^2 [\Phi(x)]^{(n-1)/2} [1-\Phi(x)]^{(n-1)/2}
\, \phi(x) \, dx](images/ap3eq71.gif)
If n is even, the function calculates the square root of the following:
where
![E(X_{(n/2)}^2) =
\frac{2}{B( \frac{n}2,\frac{n}2 ) }
\displaystyle \int_{-\infty}^\infty
x^2 [\Phi(x)]^{(n/2)-1} [1-\Phi(x)]^{n/2} \, \phi(x) \, dx](images/ap3eq72.gif)
![E(X_{((n/2)+1)}^2) =
\frac{2}{B( \frac{n}2,\frac{n}2 ) }
\displaystyle \int_{-\infty}^\infty
x^2 [\Phi(x)]^{n/2} [1-\Phi(x)]^{(n/2)-1} \, \phi(x) \, dx](images/ap3eq73.gif)
![E(X_{(n/2)}X_{((n/2)+1)}) =
\frac{n}{B( \frac{n}2,\frac{n}2 ) }
\displaystyle ...
... xy [\Phi(x)]^{(n/2)-1} [1-\Phi(y)]^{(n/2)-1}
\, \phi(x) \, \phi(y) \, dx \,dy](images/ap3eq74.gif)
For more details, refer to David (1981), Kendall and Stuart (1977, 252), and Sarhan and Greenberg (1962).
data;
do n=6 to 12;
s=stdmed(n);
put s;
output;
end;
run;
The statements produce these values:
0.4634033519 0.4587448763 0.410098592 0.4075552495 0.3719226208 0.3703544701 0.3428063408
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