![](/common/images/spacer.gif) |
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif) |
![](/images/database/grey-line.gif) |
![](/images/database/grey-line.gif) |
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif) Population moments of sampling distributions
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif) |
![](/images/database/grey-line.gif) |
![](/images/database/grey-line.gif) |
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif)
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif) |
![](/images/database/grey-line.gif) |
![](/images/database/grey-line.gif) |
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif)
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif) |
![](/images/database/grey-line.gif) |
![](/images/database/grey-line.gif) |
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif) We describe a simple technique for computing population moments of a large class of sample statistics. We assume these to be symmetric functions of n independent observations, where n is relatively large. This enables us to expand the results in powers of 1/n. The algorithm is demonstrated using Mathematica.
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif) |
![](/images/database/grey-line.gif) |
![](/images/database/grey-line.gif) |
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif)
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif) |
![](/images/database/grey-line.gif) |
![](/images/database/grey-line.gif) |
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif) population moments, symmetric polynomial, sum of independent powers
![](/common/images/spacer.gif) |
![](/common/images/spacer.gif) |