What does this simple notation mean?

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The notation X_{n:n} represents the largest order statistic in a sample of size n, often denoted as X_{(n)}. This notation is frequently encountered in statistical contexts, particularly when discussing double arrays of random variables in relation to limit theorems. It is relevant in the analysis of rank statistics, which are calculated based on the ranks of data values. The distinction between "rank statistic" and "order statistic" is crucial for understanding their applications in statistical theory and distribution calculations. This notation is particularly useful in fields such as insurance claim analysis.
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X_{n:n}

where X is a random variable. Searched the internots but couldn't find anything :frown:
 
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Some authors use this to denote the largest order statistic in a sample of size n - in notation

<br /> X_{n:n} = X_{(n)}<br />

is the largest order statistic.

You will also occasionally see this notation used in connections of double arrays of random variables, usually when a type of limit theorem is being discussed. (double array = arrangement of rows, with number of entries increasing with each row, to illustrate growing sample sizes)There may be others.
In what context did you encounter this?
 
statdad said:
Some authors use this to denote the largest order statistic in a sample of size n - in notation

<br /> X_{n:n} = X_{(n)}<br />

is the largest order statistic.

You will also occasionally see this notation used in connections of double arrays of random variables, usually when a type of limit theorem is being discussed. (double array = arrangement of rows, with number of entries increasing with each row, to illustrate growing sample sizes)There may be others.
In what context did you encounter this?

Thanks a lot. I was using search term "rank statistic" instead of "order statistic".

This is in the context of largest insurance claim.
 
A "rank statistic" is a statistic calculated based on the ranks of the data. The smallest data value has rank 1, the next smallest rank 2, and so on. Much of the calculation and theory (to determine distributions) of rank statistics can be expressed in terms of the order statistics.
 
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