BaringhausHenzeTest

BaringhausHenzeTest[data]

tests whether data follows a MultinormalDistribution using the BaringhausHenze test.

BaringhausHenzeTest[data,MultinormalDistribution[μ,Σ]]

tests whether data follows the distribution with mean vector μ and covariance matrix Σ.

BaringhausHenzeTest[data,"property"]

returns the value of "property".

Details and Options

• BaringhausHenzeTest performs a goodness-of-fit test, with null hypothesis that data was drawn from a MultinormalDistribution and alternative hypothesis that it was not.
• BaringhausHenzeTest is also known as BaringhausHenzeEppsPulley multivariate normality test, or BHEP test.
• By default, a probability value or -value is returned.
• A small -value suggests that it is unlikely that the data came from a multivariate normal distribution.
• The data can be univariate {x1,,xn} or multivariate {{x1,y1,},,{xn,yn,}}.
• The BaringhausHenze test effectively uses a test statistic Tβ based on an distance between the empirical characteristic function of decorrelated standardized data and the standard multivariate Gaussian characteristic function Tβ=Expectation[n Abs[Ψemp[t]-Ψst[t]]2,{t1,,td}], where =ProductDistribution[{NormalDistribution[0,β],d}]. »
• The β parameter is positive and determines the smoothing of the empirical distribution. It is automatically determined, but can be changed using a Method setting.
• BaringhausHenzeTest[data,MultinormalDistribution[μ,Σ],"HypothesisTestData"] returns a HypothesisTestData object htd that can be used to extract additional test results and properties using the form htd["property"].
• BaringhausHenzeTest[data,MultinormalDistribution[μ,Σ],"property"] can be used to directly give the value of "property".
• Properties related to the reporting of test results include:
•  "PValue" -value "PValueTable" formatted version of "PValue" "ShortTestConclusion" a short description of the conclusion of a test "TestConclusion" a description of the conclusion of a test "TestData" test statistic and -value "TestDataTable" formatted version of "TestData" "TestStatistic" test statistic "TestStatisticTable" formatted "TestStatistic"
• The following properties are independent of which test is being performed.
• Properties related to the data distribution include:
•  "FittedDistribution" fitted distribution of data "FittedDistributionParameters" distribution parameters of data
• The following options can be given:
•  Method Automatic the method to use for computing -values SignificanceLevel 0.05 cutoff for diagnostics and reporting
• For a test for goodness of fit, a cutoff is chosen such that is rejected only if . The value of used for the "TestConclusion" and "ShortTestConclusion" properties is controlled by the SignificanceLevel option. By default, is set to 0.05. »
• With the setting Method->"MonteCarlo", a number of datasets of the same length as the input are generated under using the fitted distribution. The EmpiricalDistribution from BaringhausHenzeTest[si,"TestStatistic"] is then used to estimate the -value. »
• The setting Method{method,"SmoothingParameter"β} allows for a custom smoothing parameter β. By default, , in which case the test is also known as the HenzeZirkler test. »

Examples

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Basic Examples(3)

Perform a test for multivariate normality:

Extract the test statistic from the BaringhausHenze test:

Obtain a formatted test table:

Scope(6)

Testing(3)

Perform a BaringhausHenze test for univariate normality:

The -value for the normal data is large compared to the -value for the non-normal data:

Perform a BaringhausHenze test for multivariate normality:

The -value for the normal data is large compared to the -value for the non-normal data:

Create a HypothesisTestData object for repeated property extraction:

The properties available for extraction:

Reporting(3)

Tabulate the results of the BaringhausHenze test:

The full test table:

A -value table:

The test statistic:

Retrieve the entries from a BaringhausHenze test table for custom reporting:

Report test conclusions using "ShortTestConclusion" and "TestConclusion":

The conclusion may differ at a different significance level:

Options(3)

Method(2)

Set the smoothing parameter β used in the BaringhausHenze test:

The smoothing parameter β has an effect on the resulting -value:

Set the random seed used in Monte Carlo-based methods:

The seed affects the state of the generator and has some effect on the resulting -value:

Find the -value using the Monte Carlo method and a custom setting of the test's parameter β:

Compare with the -value obtained with the default method:

SignificanceLevel(1)

Set the significance level used for "TestConclusion" and "ShortTestConclusion":

By default, 0.05 is used:

Applications(4)

A power (the probability of rejecting when it is false) curve for the BaringhausHenze test:

Visualize the approximate power curve:

Estimate the power of the BaringhausHenze test when the underlying distribution is a MultivariateTDistribution, the test size is 0.05, and the sample size is 43:

Check whether a continuous-time random process is a Gaussian process. Fractional Brownian motion:

The difference of logarithm of geometric Brownian motion:

The difference of logarithm of the CoxIngersollRoss process:

The weights of patients before and after a treatment study were recorded. A multivariate test of means can be used as a check in discriminating between the control and experimental groups if the data that follows is multivariate normally distributed:

Use BaringhausHenzeTest to determine if the three groups of data are multivariate normal:

The data in the Cont and FT groups are not rejected by the BaringhausHenze test. Use TTest to check whether the means of these two groups are identical:

Test the hypothesis that the data follows a standard uncorrelated binormal distribution:

Properties & Relations(5)

The BaringhausHenze test is affine invariant, provided that the linear transformation applied on the data is nonsingular:

The Mardia combined test for normality also shares the same property:

Under the test statistic asymptotically follows a LogNormalDistribution:

Test whether the test statistic follows a distribution from the LogNormalDistribution family:

Show the histogram of the test statistic with the fitted lognormal density:

The BaringhausHenzeTest statistic is based on a distance between the sample empirical characteristic function and the characteristic function under the null hypothesis:

Define the empirical characteristic function and compute the distance:

Compare to the value of the test statistic reported by BaringhausHenzeTest:

For samples with degenerate sample covariance function, the test statistic gives its maximum value of 4 n:

The corresponding -value equals zero:

For small values of the smoothing parameter β, the BaringhausHenze test is more sensitive to the tail behavior:

The fraction of rejections of the multivariate normality hypothesis as a function of the smoothing parameter:

Compare to the Mardia tests:

Neat Examples(1)

Define a bivariate distribution with standard normal marginals:

Marginal distributions are standard Gaussians:

Sample from the bivariate distribution:

Plot the histograms of its components and compare them with the density function of standard normal:

The distribution is not jointly Gaussian:

Show the sampled random vectors on the plane:

Wolfram Research (2015), BaringhausHenzeTest, Wolfram Language function, https://reference.wolfram.com/language/ref/BaringhausHenzeTest.html.

Text

Wolfram Research (2015), BaringhausHenzeTest, Wolfram Language function, https://reference.wolfram.com/language/ref/BaringhausHenzeTest.html.

CMS

Wolfram Language. 2015. "BaringhausHenzeTest." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/BaringhausHenzeTest.html.

APA

Wolfram Language. (2015). BaringhausHenzeTest. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/BaringhausHenzeTest.html

BibTeX

@misc{reference.wolfram_2024_baringhaushenzetest, author="Wolfram Research", title="{BaringhausHenzeTest}", year="2015", howpublished="\url{https://reference.wolfram.com/language/ref/BaringhausHenzeTest.html}", note=[Accessed: 26-May-2024 ]}

BibLaTeX

@online{reference.wolfram_2024_baringhaushenzetest, organization={Wolfram Research}, title={BaringhausHenzeTest}, year={2015}, url={https://reference.wolfram.com/language/ref/BaringhausHenzeTest.html}, note=[Accessed: 26-May-2024 ]}