org.apache.commons.math4.stat.descriptive.rank

## Enum Percentile.EstimationType

• All Implemented Interfaces:
Serializable, Comparable<Percentile.EstimationType>
Enclosing class:
Percentile

public static enum Percentile.EstimationType
extends Enum<Percentile.EstimationType>
An enum for various estimation strategies of a percentile referred in wikipedia on quantile with the names of enum matching those of types mentioned in wikipedia.

Each enum corresponding to the specific type of estimation in wikipedia implements the respective formulae that specializes in the below aspects

• An index method to calculate approximate index of the estimate
• An estimate method to estimate a value found at the earlier computed index
• A minLimit on the quantile for which first element of sorted input is returned as an estimate
• A maxLimit on the quantile for which last element of sorted input is returned as an estimate

Users can now create Percentile by explicitly passing this enum; such as by invoking Percentile.withEstimationType(EstimationType)

References:

• ### Enum Constant Summary

Enum Constants
Enum Constant and Description
LEGACY
This is the default type used in the Percentile.This method has the following formulae for index and estimates
\begin{align} &index = (N+1)p\ \\ &estimate = x_{\lceil h\,-\,1/2 \rceil} \\ &minLimit = 0 \\ &maxLimit = 1 \\ \end{align}
R_1
The method R_1 has the following formulae for index and estimates
\begin{align} &index= Np + 1/2\, \\ &estimate= x_{\lceil h\,-\,1/2 \rceil} \\ &minLimit = 0 \\ \end{align}
R_2
The method R_2 has the following formulae for index and estimates
\begin{align} &index= Np + 1/2\, \\ &estimate=\frac{x_{\lceil h\,-\,1/2 \rceil} + x_{\lfloor h\,+\,1/2 \rfloor}}{2} \\ &minLimit = 0 \\ &maxLimit = 1 \\ \end{align}
R_3
The method R_3 has the following formulae for index and estimates
\begin{align} &index= Np \\ &estimate= x_{\lfloor h \rceil}\, \\ &minLimit = 0.5/N \\ \end{align}
R_4
The method R_4 has the following formulae for index and estimates
\begin{align} &index= Np\, \\ &estimate= x_{\lfloor h \rfloor} + (h - \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h \rfloor}) \\ &minLimit = 1/N \\ &maxLimit = 1 \\ \end{align}
R_5
The method R_5 has the following formulae for index and estimates
\begin{align} &index= Np + 1/2\\ &estimate= x_{\lfloor h \rfloor} + (h - \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h \rfloor}) \\ &minLimit = 0.5/N \\ &maxLimit = (N-0.5)/N \end{align}
R_6
The method R_6 has the following formulae for index and estimates
\begin{align} &index= (N + 1)p \\ &estimate= x_{\lfloor h \rfloor} + (h - \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h \rfloor}) \\ &minLimit = 1/(N+1) \\ &maxLimit = N/(N+1) \\ \end{align}
R_7
The method R_7 implements Microsoft Excel style computation has the following formulae for index and estimates.
\begin{align} &index = (N-1)p + 1 \\ &estimate = x_{\lfloor h \rfloor} + (h - \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h \rfloor}) \\ &minLimit = 0 \\ &maxLimit = 1 \\ \end{align}
R_8
The method R_8 has the following formulae for index and estimates
\begin{align} &index = (N + 1/3)p + 1/3 \\ &estimate = x_{\lfloor h \rfloor} + (h - \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h \rfloor}) \\ &minLimit = (2/3)/(N+1/3) \\ &maxLimit = (N-1/3)/(N+1/3) \\ \end{align}
R_9
The method R_9 has the following formulae for index and estimates
\begin{align} &index = (N + 1/4)p + 3/8\\ &estimate = x_{\lfloor h \rfloor} + (h - \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h \rfloor}) \\ &minLimit = (5/8)/(N+1/4) \\ &maxLimit = (N-3/8)/(N+1/4) \\ \end{align}
• ### Method Summary

All Methods
Modifier and Type Method and Description
protected double estimate(double[] work, int[] pivotsHeap, double pos, int length, KthSelector selector)
Estimation based on Kth selection.
double evaluate(double[] work, double p, KthSelector selector)
Evaluate method to compute the percentile for a given bounded array.
protected double evaluate(double[] work, int[] pivotsHeap, double p, KthSelector selector)
Evaluate method to compute the percentile for a given bounded array using earlier computed pivots heap.
This basically calls the index and then estimate functions to return the estimated percentile value.
protected abstract double index(double p, int length)
Finds the index of array that can be used as starting index to estimate percentile.
static Percentile.EstimationType valueOf(String name)
Returns the enum constant of this type with the specified name.
static Percentile.EstimationType[] values()
Returns an array containing the constants of this enum type, in the order they are declared.
• ### Enum Constant Detail

• #### LEGACY

public static final Percentile.EstimationType LEGACY
This is the default type used in the Percentile.This method has the following formulae for index and estimates
\begin{align} &index = (N+1)p\ \\ &estimate = x_{\lceil h\,-\,1/2 \rceil} \\ &minLimit = 0 \\ &maxLimit = 1 \\ \end{align}
• #### R_1

public static final Percentile.EstimationType R_1
The method R_1 has the following formulae for index and estimates
\begin{align} &index= Np + 1/2\, \\ &estimate= x_{\lceil h\,-\,1/2 \rceil} \\ &minLimit = 0 \\ \end{align}
• #### R_2

public static final Percentile.EstimationType R_2
The method R_2 has the following formulae for index and estimates
\begin{align} &index= Np + 1/2\, \\ &estimate=\frac{x_{\lceil h\,-\,1/2 \rceil} + x_{\lfloor h\,+\,1/2 \rfloor}}{2} \\ &minLimit = 0 \\ &maxLimit = 1 \\ \end{align}
• #### R_3

public static final Percentile.EstimationType R_3
The method R_3 has the following formulae for index and estimates
\begin{align} &index= Np \\ &estimate= x_{\lfloor h \rceil}\, \\ &minLimit = 0.5/N \\ \end{align}
• #### R_4

public static final Percentile.EstimationType R_4
The method R_4 has the following formulae for index and estimates
\begin{align} &index= Np\, \\ &estimate= x_{\lfloor h \rfloor} + (h - \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h \rfloor}) \\ &minLimit = 1/N \\ &maxLimit = 1 \\ \end{align}
• #### R_5

public static final Percentile.EstimationType R_5
The method R_5 has the following formulae for index and estimates
\begin{align} &index= Np + 1/2\\ &estimate= x_{\lfloor h \rfloor} + (h - \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h \rfloor}) \\ &minLimit = 0.5/N \\ &maxLimit = (N-0.5)/N \end{align}
• #### R_6

public static final Percentile.EstimationType R_6
The method R_6 has the following formulae for index and estimates
\begin{align} &index= (N + 1)p \\ &estimate= x_{\lfloor h \rfloor} + (h - \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h \rfloor}) \\ &minLimit = 1/(N+1) \\ &maxLimit = N/(N+1) \\ \end{align}

Note: This method computes the index in a manner very close to the default Commons Math Percentile existing implementation. However the difference to be noted is in picking up the limits with which first element (p<1(N+1)) and last elements (p>N/(N+1))are done. While in default case; these are done with p=0 and p=1 respectively.

• #### R_7

public static final Percentile.EstimationType R_7
The method R_7 implements Microsoft Excel style computation has the following formulae for index and estimates.
\begin{align} &index = (N-1)p + 1 \\ &estimate = x_{\lfloor h \rfloor} + (h - \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h \rfloor}) \\ &minLimit = 0 \\ &maxLimit = 1 \\ \end{align}
• #### R_8

public static final Percentile.EstimationType R_8
The method R_8 has the following formulae for index and estimates
\begin{align} &index = (N + 1/3)p + 1/3 \\ &estimate = x_{\lfloor h \rfloor} + (h - \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h \rfloor}) \\ &minLimit = (2/3)/(N+1/3) \\ &maxLimit = (N-1/3)/(N+1/3) \\ \end{align}

As per Ref [2,3] this approach is most recommended as it provides an approximate median-unbiased estimate regardless of distribution.

• #### R_9

public static final Percentile.EstimationType R_9
The method R_9 has the following formulae for index and estimates
\begin{align} &index = (N + 1/4)p + 3/8\\ &estimate = x_{\lfloor h \rfloor} + (h - \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h \rfloor}) \\ &minLimit = (5/8)/(N+1/4) \\ &maxLimit = (N-3/8)/(N+1/4) \\ \end{align}
• ### Method Detail

• #### values

public static Percentile.EstimationType[] values()
Returns an array containing the constants of this enum type, in the order they are declared. This method may be used to iterate over the constants as follows:
for (Percentile.EstimationType c : Percentile.EstimationType.values())
System.out.println(c);

Returns:
an array containing the constants of this enum type, in the order they are declared
• #### valueOf

public static Percentile.EstimationType valueOf(String name)
Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an enum constant in this type. (Extraneous whitespace characters are not permitted.)
Parameters:
name - the name of the enum constant to be returned.
Returns:
the enum constant with the specified name
Throws:
IllegalArgumentException - if this enum type has no constant with the specified name
NullPointerException - if the argument is null
• #### index

protected abstract double index(double p,
int length)
Finds the index of array that can be used as starting index to estimate percentile. The calculation of index calculation is specific to each Percentile.EstimationType.
Parameters:
p - the pth quantile
length - the total number of array elements in the work array
Returns:
a computed real valued index as explained in the wikipedia
• #### estimate

protected double estimate(double[] work,
int[] pivotsHeap,
double pos,
int length,
KthSelector selector)
Estimation based on Kth selection. This may be overridden in specific enums to compute slightly different estimations.
Parameters:
work - array of numbers to be used for finding the percentile
pos - indicated positional index prior computed from calling index(double, int)
pivotsHeap - an earlier populated cache if exists; will be used
length - size of array considered
selector - a KthSelector used for pivoting during search
Returns:
estimated percentile
• #### evaluate

protected double evaluate(double[] work,
int[] pivotsHeap,
double p,
KthSelector selector)
Evaluate method to compute the percentile for a given bounded array using earlier computed pivots heap.
This basically calls the index and then estimate functions to return the estimated percentile value.
Parameters:
work - array of numbers to be used for finding the percentile
pivotsHeap - a prior cached heap which can speed up estimation
p - the pth quantile to be computed
selector - a KthSelector used for pivoting during search
Returns:
estimated percentile
Throws:
OutOfRangeException - if p is out of range
NullArgumentException - if work array is null
• #### evaluate

public double evaluate(double[] work,
double p,
KthSelector selector)
Evaluate method to compute the percentile for a given bounded array. This basically calls the index and then estimate functions to return the estimated percentile value. Please note that this method does not make use of cached pivots.
Parameters:
work - array of numbers to be used for finding the percentile
p - the pth quantile to be computed
selector - a KthSelector used for pivoting during search
Returns:
estimated percentile
Throws:
OutOfRangeException - if length or p is out of range
NullArgumentException - if work array is null