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 more details on sample quantiles see option method below.
|
•
|
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).
|
|
|
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.
|
1.
|
, where ;
|
2.
|
, where ;
|
3.
|
;
|
4.
|
;
|
5.
|
;
|
6.
|
;
|
7.
|
; (default method)
|
8.
|
.
|
|
|
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 .
>
|
|
| (1) |
Use numeric parameters.
>
|
|
| (2) |
>
|
|
| (3) |
>
|
|
| (4) |
Generate a random sample of size 100000 drawn from the above distribution and compute the sample quantile.
>
|
|
>
|
|
| (5) |
Compute the standard error of the sample quantile for the normal distribution with parameters 5 and 2.
>
|
|
| (6) |
>
|
|
>
|
|
| (7) |
>
|
|
| (8) |
Create two normal random variables and compute the quantiles of their sum.
>
|
|
>
|
|
>
|
|
| (9) |
>
|
|
| (10) |
Verify this using simulation.
>
|
|
>
|
|
| (11) |
Compute the quantile of a weighted data set.
>
|
|
>
|
|
>
|
|
| (12) |
>
|
|
| (13) |
Consider the following Matrix data set.
>
|
|
| (14) |
We compute the quantile of each of the columns.
>
|
|
| (15) |
|
|
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.
|
|
|