# MarchenkoPasturDistribution

represents a MarchenkoPastur distribution with asymptotic ratio and scale parameter .

represents a MarchenkoPastur distribution with unit scale parameter.

# Examples

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## Basic Examples(3)

Probability density function:

Cumulative distribution function:

Mean and variance:

## Scope(7)

Generate a sample of pseudorandom numbers from a MarchenkoPastur distribution with :

Compare its histogram to the PDF:

Generate a sample of pseudorandom numbers from a MarchenkoPastur distribution with :

Compare its cumulative histogram to the CDF:

Distribution parameters estimation:

Estimate the distribution parameters from sample data:

Compare the cumulative histogram of the sample with the CDF of the estimated distribution:

Skewness and kurtosis depend only on :

Different moments with closed forms as functions of parameters:

Closed form for symbolic order:

Closed form for symbolic order:

Hazard function:

Quantile function:

Consistent use of Quantity in parameters yields QuantityDistribution:

Find the median area:

## Applications(1)

Use MatrixPropertyDistribution to represent the eigenvalues of a Wishart random matrix with identity covariance:

The spectral density converges to the pdf of in the limit of large and with the finite ratio :

## Properties & Relations(3)

MarchenkoPasturDistribution is closed under scaling by a positive factor:

MarchenkoPasturDistribution has an atomic weight at 0 when :

MarchenkoPasturDistribution is the limiting distribution of eigenvalues of Wishart matrices. The atomic weight at occurs when the Wishart matrix is singular. Generate a singular Wishart matrix with identity covariance and compute the scaled eigenvalues:

Fit MarchenkoPasturDistribution to the eigenvalues:

Compare the cumulative histogram of the eigenvalues with the CDF:

## Possible Issues(1)

MarchenkoPastur distribution with is a mixed type distribution, which is neither continuous nor discrete:

The CDF for such MarchenkoPastur distributions is discontinuous at :

The probability density function for MarchenkoPastur distribution with is not defined, and PDF returns unevaluated:

Differentiation of the CDF results in a function that does not integrate to one:

Computations with mixed type distributions are fully supported. Compute special moments:

Estimate parameters of Marchenko-Pastur distribution:

Wolfram Research (2015), MarchenkoPasturDistribution, Wolfram Language function, https://reference.wolfram.com/language/ref/MarchenkoPasturDistribution.html (updated 2016).

#### Text

Wolfram Research (2015), MarchenkoPasturDistribution, Wolfram Language function, https://reference.wolfram.com/language/ref/MarchenkoPasturDistribution.html (updated 2016).

#### CMS

Wolfram Language. 2015. "MarchenkoPasturDistribution." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2016. https://reference.wolfram.com/language/ref/MarchenkoPasturDistribution.html.

#### APA

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

#### BibTeX

@misc{reference.wolfram_2024_marchenkopasturdistribution, author="Wolfram Research", title="{MarchenkoPasturDistribution}", year="2016", howpublished="\url{https://reference.wolfram.com/language/ref/MarchenkoPasturDistribution.html}", note=[Accessed: 12-August-2024 ]}

#### BibLaTeX

@online{reference.wolfram_2024_marchenkopasturdistribution, organization={Wolfram Research}, title={MarchenkoPasturDistribution}, year={2016}, url={https://reference.wolfram.com/language/ref/MarchenkoPasturDistribution.html}, note=[Accessed: 12-August-2024 ]}