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Statistics[Quantile] - compute quantiles
Calling Sequence
Quantile(A, p, ds_options)
Quantile(X, p, rv_options)
Parameters
A
-
Array or Matrix data set; data sample
X
algebraic; random variable or distribution
p
algebraic; probability
ds_options
(optional) equation(s) of the form option=value where option is one of ignore, method, or weights; specify options for computing the quantile of a data set
rv_options
(optional) equation of the form numeric=value; specifies options for computing the quantile of a random variable
Description
The Quantile function computes the quantile corresponding to the given probability p for the specified random variable or data set.
For a real valued random variable X with distribution function , and any between 0 and 1, the th quantile of is defined as . For continuous random variables this is equivalent to the inverse distribution function.
For more details on sample quantiles see option method below.
The first parameter can be a data set (represented as an Array or a Matrix data set), a distribution (see Statistics[Distribution]), a random variable, or an algebraic expression involving random variables (see Statistics[RandomVariable]).
The second parameter p is the probability.
Computation
All computations involving data are performed in floating-point; therefore, all data provided must have type/realcons and all returned solutions are floating-point, even if the problem is specified with exact values.
By default, all computations involving random variables are performed symbolically (see option numeric below).
For more information about computation in the Statistics package, see the Statistics[Computation] help page.
Data Set Options
The ds_options argument can contain one or more of the options shown below. More information for some options is available in the Statistics[DescriptiveStatistics] help page.
ignore=truefalse -- This option controls how missing data is handled by the Quantile command. Missing items are represented by undefined or Float(undefined). So, if ignore=false and A contains missing data, the Quantile command will return undefined. If ignore=true all missing items in A will be ignored. The default value is false.
weights=Vector -- Data weights. The number of elements in the weights array must be equal to the number of elements in the original data sample. By default all elements in A are assigned weight .
method=integer[1..8] -- Method for calculating the quantiles. Let n denote the number of non-missing elements in A and for let denotes the ith order statistic of A. The first two methods for calculating quantiles are defined as follows.
, where ;
The remaining quantiles are calculated in the form , where , , and is one of the quantities given below.
;
; (default method)
.
Random Variable Options
The rv_options argument can contain one or more of the options shown below. More information for some options is available in the Statistics[RandomVariables] help page.
numeric=truefalse -- By default, the quantile is computed using exact arithmetic. To compute the quantile numerically, specify the numeric or numeric = true option.
Compatibility
The A parameter was updated in Maple 16.
Examples
Compute the quantile of the Weibull distribution with parameters and .
Use numeric parameters.
Generate a random sample of size 100000 drawn from the above distribution and compute the sample quantile.
Compute the standard error of the sample quantile for the normal distribution with parameters 5 and 2.
Create two normal random variables and compute the quantiles of their sum.
Verify this using simulation.
Compute the quantile of a weighted data set.
Consider the following Matrix data set.
We compute the quantile of each of the columns.
See Also
Statistics, Statistics[Computation], Statistics[CumulativeDistributionFunction], Statistics[Decile]. Statistics[DescriptiveStatistics], Statistics[Distributions], Statistics[ExpectedValue], Statistics[Percentile], Statistics[Quartile], Statistics[RandomVariables], Statistics[StandardError]
References
Stuart, Alan, and Ord, Keith. Kendall's Advanced Theory of Statistics. 6th ed. London: Edward Arnold, 1998. Vol. 1: Distribution Theory.
Hyndman, R.J., and Fan, Y. "Sample Quantiles in Statistical Packages." American Statistician, Vol. 50. (1996): 361-365.
Download Help Document