Class Kurtosis
- java.lang.Object
-
- org.apache.commons.statistics.descriptive.Kurtosis
-
- All Implemented Interfaces:
DoubleConsumer
,DoubleSupplier
,IntSupplier
,LongSupplier
,DoubleStatistic
,StatisticAccumulator<Kurtosis>
,StatisticResult
public final class Kurtosis extends Object implements DoubleStatistic, StatisticAccumulator<Kurtosis>
Computes the kurtosis of the available values. The kurtosis is defined as:\[ \operatorname{Kurt} = \operatorname{E}\left[ \left(\frac{X-\mu}{\sigma}\right)^4 \right] = \frac{\mu_4}{\sigma^4} \]
where \( \mu \) is the mean of \( X \), \( \sigma \) is the standard deviation of \( X \), \( \operatorname{E} \) represents the expectation operator, and \( \mu_4 \) is the fourth central moment.
The default implementation uses the following definition of the sample kurtosis:
\[ G_2 = \frac{k_4}{k_2^2} = \; \frac{n-1}{(n-2)\,(n-3)} \left[(n+1)\,\frac{m_4}{m_{2}^2} - 3\,(n-1) \right] \]
where \( k_4 \) is the unique symmetric unbiased estimator of the fourth cumulant, \( k_2 \) is the symmetric unbiased estimator of the second cumulant (i.e. the sample variance), \( m_4 \) is the fourth sample moment about the mean, \( m_2 \) is the second sample moment about the mean, \( \overline{x} \) is the sample mean, and \( n \) is the number of samples.
- The result is
NaN
if less than 4 values are added. - The result is
NaN
if any of the values isNaN
or infinite. - The result is
NaN
if the sum of the fourth deviations from the mean is infinite.
The default computation is for the adjusted Fisher–Pearson standardized moment coefficient \( G_2 \). If the
biased
option is enabled the following equation applies:\[ g_2 = \frac{m_4}{m_2^2} - 3 = \frac{\tfrac{1}{n} \sum_{i=1}^n (x_i-\overline{x})^4} {\left[\tfrac{1}{n} \sum_{i=1}^n (x_i-\overline{x})^2 \right]^2} - 3 \]
In this case the computation only requires 2 values are added (i.e. the result is
NaN
if less than 2 values are added).Note that the computation requires division by the second central moment \( m_2 \). If this is effectively zero then the result is
NaN
. This occurs when the value \( m_2 \) approaches the machine precision of the mean: \( m_2 \le (m_1 \times 10^{-15})^2 \).The
accept(double)
method uses a recursive updating algorithm.The
of(double...)
method uses a two-pass algorithm, starting with computation of the mean, and then computing the sum of deviations in a second pass.Note that adding values using
accept
and then executinggetAsDouble
will sometimes give a different result than executingof
with the full array of values. The former approach should only be used when the full array of values is not available.Supports up to 263 (exclusive) observations. This implementation does not check for overflow of the count.
This class is designed to work with (though does not require) streams.
Note that this instance is not synchronized. If multiple threads access an instance of this class concurrently, and at least one of the threads invokes the
accept
orcombine
method, it must be synchronized externally.However, it is safe to use
accept
andcombine
asaccumulator
andcombiner
functions ofCollector
on a parallel stream, because the parallel instance ofStream.collect()
provides the necessary partitioning, isolation, and merging of results for safe and efficient parallel execution.- Since:
- 1.1
- See Also:
- Kurtosis (Wikipedia)
-
-
Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
accept(double value)
Updates the state of the statistic to reflect the addition ofvalue
.Kurtosis
combine(Kurtosis other)
Combines the state of theother
statistic into this one.static Kurtosis
create()
Creates an instance.double
getAsDouble()
Gets the kurtosis of all input values.static Kurtosis
of(double... values)
Returns an instance populated using the inputvalues
.static Kurtosis
of(int... values)
Returns an instance populated using the inputvalues
.static Kurtosis
of(long... values)
Returns an instance populated using the inputvalues
.Kurtosis
setBiased(boolean v)
Sets the value of the biased flag.-
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
-
Methods inherited from interface java.util.function.DoubleConsumer
andThen
-
Methods inherited from interface org.apache.commons.statistics.descriptive.StatisticResult
getAsBigInteger, getAsInt, getAsLong
-
-
-
-
Method Detail
-
create
public static Kurtosis create()
Creates an instance.The initial result is
NaN
.- Returns:
Kurtosis
instance.
-
of
public static Kurtosis of(double... values)
Returns an instance populated using the inputvalues
.Note:
Kurtosis
computed usingaccept
may be different from this instance.- Parameters:
values
- Values.- Returns:
Kurtosis
instance.
-
of
public static Kurtosis of(int... values)
Returns an instance populated using the inputvalues
.Note:
Kurtosis
computed usingaccept
may be different from this instance.- Parameters:
values
- Values.- Returns:
Kurtosis
instance.
-
of
public static Kurtosis of(long... values)
Returns an instance populated using the inputvalues
.Note:
Kurtosis
computed usingaccept
may be different from this instance.- Parameters:
values
- Values.- Returns:
Kurtosis
instance.
-
accept
public void accept(double value)
Updates the state of the statistic to reflect the addition ofvalue
.- Specified by:
accept
in interfaceDoubleConsumer
- Parameters:
value
- Value.
-
getAsDouble
public double getAsDouble()
Gets the kurtosis of all input values.When fewer than 4 values have been added, the result is
NaN
.- Specified by:
getAsDouble
in interfaceDoubleSupplier
- Returns:
- kurtosis of all values.
-
combine
public Kurtosis combine(Kurtosis other)
Description copied from interface:StatisticAccumulator
Combines the state of theother
statistic into this one.- Specified by:
combine
in interfaceStatisticAccumulator<Kurtosis>
- Parameters:
other
- Another statistic to be combined.- Returns:
this
instance after combiningother
.
-
setBiased
public Kurtosis setBiased(boolean v)
Sets the value of the biased flag. The default value isfalse
. SeeKurtosis
for details on the computing algorithm.This flag only controls the final computation of the statistic. The value of this flag will not affect compatibility between instances during a
combine
operation.- Parameters:
v
- Value.- Returns:
this
instance
-
-