order statistics

Alternatively, they show that order statistics contain a significant amount of information.

The input is sorted by context, which removes any high order statistics.

Nor will rank order statistics distinguish between these series.

More formally, they are distinct because the order statistics are not independent.

It is clearly a function of the order statistics:

If sorting is allowed, previously known order statistics finding algorithms may be exploited.

We seek the probability of some event A defined in terms of these order statistics.

Note that the order statistics also satisfy .

Perhaps surprisingly, the joint density of the n order statistics turns out to be constant:

Other types of bounds can be generated by varying the rate of violation for the order statistics.

However the above algorithms require fewer comparisons than the order statistics algorithms.

In that case, the sample median is usually defined as the midpoint of the interval delimited by the 3rd and 4th order statistics.

These are both linear combinations of order statistics, and the median is therefore a simple example of an L-estimator.

A similar important statistic in exploratory data analysis that is simply related to the order statistics is the sample interquartile range.

In particular, the range is a linear function of order statistics, which brings it into the scope of L-estimation.

The computer enters this data into a complex series of computer operations based on a system of applied mathematics called order statistics.

An interesting question is how well the order statistics perform as estimators of the quantiles of the underlying distribution.

If there are equiprobable outcomes with the corresponding payoffs given by the order statistics .

The range is a simple function of the sample maximum and minimum and these are specific examples of order statistics.

One rearranges the expected normal order statistics accordingly, getting the rankits of this data set:

"Statistical inference using extreme order statistics", Annals of Statistics, 3, 119-131.

Polyspectra techniques utilize higher order statistics in order to compute the equalizer.

The Laplace transform of order statistics sampled from an Erlang distribution via a path counting method.

Francis Galton emphasized order statistics and quantiles.

The order statistic medians are the medians of the order statistics of the distribution.