# Bibliography On Higher Order Statistics Exponential Distribution

In statistics, the *k*th **order statistic** of a statistical sample is equal to its *k*th-smallest value.^{[1]} Together with rank statistics, order statistics are among the most fundamental tools in non-parametric statistics and inference.

Important special cases of the order statistics are the minimum and maximum value of a sample, and (with some qualifications discussed below) the sample median and other sample quantiles.

When using probability theory to analyze order statistics of random samples from a continuous distribution, the cumulative distribution function is used to reduce the analysis to the case of order statistics of the uniform distribution.

## Notation and examples[edit]

For example, suppose that four numbers are observed or recorded, resulting in a sample of size 4. If the sample values are

- 6, 9, 3, 8,

they will usually be denoted

where the subscript *i* in indicates simply the order in which the observations were recorded and is usually assumed not to be significant. A case when the order is significant is when the observations are part of a time series.

The order statistics would be denoted

where the subscript (*i*) enclosed in parentheses indicates the *i*th order statistic of the sample.

The **first order statistic** (or **smallest order statistic**) is always the minimum of the sample, that is,

where, following a common convention, we use upper-case letters to refer to random variables, and lower-case letters (as above) to refer to their actual observed values.

Similarly, for a sample of size *n*, the *n*th order statistic (or **largest order statistic**) is the maximum, that is,

The sample range is the difference between the maximum and minimum. It is a function of the order statistics:

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

The sample median may or may not be an order statistic, since there is a single middle value only when the number *n* of observations is odd. More precisely, if *n* = 2*m*+1 for some integer *m*, then the sample median is and so is an order statistic. On the other hand, when *n* is even, *n* = 2*m* and there are two middle values, and , and the sample median is some function of the two (usually the average) and hence not an order statistic. Similar remarks apply to all sample quantiles.

## Probabilistic analysis[edit]

Given any random variables *X*_{1}, *X*_{2}..., *X*_{n}, the order statistics X_{(1)}, X_{(2)}, ..., X_{(n)} are also random variables, defined by sorting the values (realizations) of *X*_{1}, ..., *X*_{n} in increasing order.

When the random variables *X*_{1}, *X*_{2}..., *X*_{n} form a sample they are independent and identically distributed. This is the case treated below. In general, the random variables *X*_{1}, ..., *X*_{n} can arise by sampling from more than one population. Then they are independent, but not necessarily identically distributed, and their joint probability distribution is given by the Bapat–Beg theorem.

From now on, we will assume that the random variables under consideration are continuous and, where convenient, we will also assume that they have a probability density function (that is, they are absolutely continuous). The peculiarities of the analysis of distributions assigning mass to points (in particular, discrete distributions) are discussed at the end.

### Probability distributions of order statistics[edit]

#### Order statistics sampled from a uniform distribution[edit]

In this section we show that the order statistics of the uniform distribution on the unit interval have marginal distributions belonging to the Beta distribution family. We also give a simple method to derive the joint distribution of any number of order statistics, and finally translate these results to arbitrary continuous distributions using the cdf.

We assume throughout this section that is a random sample drawn from a continuous distribution with cdf . Denoting we obtain the corresponding random sample from the standard uniform distribution. Note that the order statistics also satisfy .

The probability of the order statistic falling in the interval is equal to^{[2]}

that is, the *k*th order statistic of the uniform distribution is a Beta random variable.^{[2]}^{[3]}

The proof of these statements is as follows. For to be between *u* and *u* + *du*, it is necessary that exactly *k* − 1 elements of the sample are smaller than *u*, and that at least one is between *u* and *u* + d*u*. The probability that more than one is in this latter interval is already , so we have to calculate the probability that exactly *k* − 1, 1 and *n* − *k* observations fall in the intervals , and respectively. This equals (refer to multinomial distribution for details)

and the result follows.

The mean of this distribution is *k* / (*n* + 1).

#### The joint distribution of the order statistics of the uniform distribution[edit]

Similarly, for *i* < *j*, the joint probability density function of the two order statistics *U*_{(i)} < *U*_{(j)} can be shown to be

which is (up to terms of higher order than ) the probability that *i* − 1, 1, *j* − 1 − *i*, 1 and *n* − *j* sample elements fall in the intervals , , , , respectively.

One reasons in an entirely analogous way to derive the higher-order joint distributions. Perhaps surprisingly, the joint density of the *n* order statistics turns out to be *constant*:

One way to understand this is that the unordered sample does have constant density equal to 1, and that there are *n*! different permutations of the sample corresponding to the same sequence of order statistics. This is related to the fact that 1/*n*! is the volume of the region .

#### Order statistics sampled from an exponential distribution[edit]

For random samples from an exponential distribution with parameter *λ* the order statistics *X*_{(i)} for *i* = 1,2,3, ..., *n* each have distribution

where the *Z*_{j} are iid standard exponential random variables (i.e. with rate parameter 1). This result was first published by Alfréd Rényi.^{[4]}^{[5]}

#### Order statistics sampled from an Erlang distribution[edit]

The Laplace transform of order statistics sampled from an Erlang distribution via a path counting method.^{[6]}

#### The joint distribution of the order statistics of an absolutely continuous distribution[edit]

If *F*_{X} is absolutely continuous, it has a density such that , and we can use the substitutions

and

to derive the following probability density functions (pdfs) for the order statistics of a sample of size *n* drawn from the distribution of *X*:

- where

- where

## Application: confidence intervals for quantiles[edit]

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

### A small-sample-size example[edit]

The simplest case to consider is how well the sample median estimates the population median.

As an example, consider a random sample of size 6. In that case, the sample median is usually defined as the midpoint of the interval delimited by the 3rd and 4th order statistics. However, we know from the preceding discussion that the probability that this interval actually contains the population median is

Although the sample median is probably among the best distribution-independent point estimates of the population median, what this example illustrates is that it is not a particularly good one in absolute terms. In this particular case, a better confidence interval for the median is the one delimited by the 2nd and 5th order statistics, which contains the population median with probability

With such a small sample size, if one wants at least 95% confidence, one is reduced to saying that the median is between the minimum and the maximum of the 6 observations with probability 31/32 or approximately 97%. Size 6 is, in fact, the smallest sample size such that the interval determined by the minimum and the maximum is at least a 95% confidence interval for the population median.

### Large sample sizes[edit]

For the uniform distribution, as *n* tends to infinity, the *p*^{th} sample quantile is asymptotically normally distributed, since it is approximated by

For a general distribution *F* with a continuous non-zero density at *F*^{ −1}(*p*), a similar asymptotic normality applies:

where *f* is the density function, and *F*^{ −1} is the quantile function associated with *F*. One of the first people to mention and prove this result was Frederick Mosteller in his seminal paper in 1946.^{[7]} Further research lead in the 1960s to the Bahadur representation which provides information about the errorbounds.

An interesting observation can be made in the case where the distribution is symmetric, and the population median equals the population mean. In this case, the sample mean, by the central limit theorem, is also asymptotically normally distributed, but with variance σ^{2}*/n* instead. This asymptotic analysis suggests that the mean outperforms the median in cases of low kurtosis, and vice versa. For example, the median achieves better confidence intervals for the Laplace distribution, while the mean performs better for *X* that are normally distributed.

#### Proof[edit]

It can be shown that

In case the underlying distribution of a sample is normal, a substantial literature has been devoted to the distribution of quantities such as $(X_{(i)} - u)/v$ and $(X_{(i)} - u)/w$, where $X_{(i)}$ denotes the $i$th ordered observation, $u$ and $v$ are location and scale statistics of the sample, or one is a location or scale parameter and $w$ is an independent scale statistic. The case $i = 1$ or $n$ has been frequently studied in view of the great importance of extreme values in physical phenomena and also with a view to testing outlying observations or the normality of the distribution. Bibliographical references will be found in Savage [10] and, as far as the general problem of testing outliers is concerned, in Ferguson [4]; references to recent literature include Dixon [1], [2], Grubbs [5], Pillai and Tienzo [9]. Thompson [12] has studied the distribution of $(X_i, - \bar{X})/s$ where $X_i$ is one observation picked at random among the sample, and this statistic has been used in the study of outliers; Laurent has generalized Thompson's distribution to the case of a subsample picked at random among a sample [7], then to the multivariate case and the general linear hypothesis [8]. Thompson's distribution is not only the marginal distribution of $(X_i - \bar{X}/s$ but its conditional distribution, given the sufficient statistic $(\bar{X}, s)$, hence it provides the distribution of $X_i$ given $\bar{X}, s$, and, using the Rao-Blackwell-Lehmann-Scheffe theorem, gives a way of obtaining a minimum variance unbiased estimate of any estimable function of the parameters of a normal distribution for which an unbiased estimate depending on one observation is available, a fact that has been exploited in sampling inspection by variable. The present paper presents an analogue to Thompson's distribution in case the underlying distribution of a sample is exponential (the exponential model is nowadays widely used in Failure and Queuing Theories). Such a distribution makes it possible to obtain minimum variance unbiased estimates of functions of the parameters of the exponential distribution. Here an estimate is provided for the survival function $P(X > x) = S(x)$ and its powers. As an application of these results the probability distribution of the "reduced" $i$th ordered observation in a sample and that of the reduced range are derived. For possible applications to testing outliers or exponentially the reader is invited to refer to the bibliography.

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