Generalized Extreme Value Distribution

Fit, evaluate, and generate random samples from generalized extreme value distribution

Functions

`makedist`	Create probability distribution object
`fitdist`	Fit probability distribution object to data
`distributionFitter`	Open Distribution Fitter app

`cdf`	Cumulative distribution function
`icdf`	Inverse cumulative distribution function
`iqr`	Interquartile range
`mean`	Mean of probability distribution
`median`	Median of probability distribution
`negloglik`	Negative loglikelihood of probability distribution
`paramci`	Confidence intervals for probability distribution parameters
`pdf`	Probability density function
`proflik`	Profile likelihood function for probability distribution
`random`	Random numbers
`std`	Standard deviation of probability distribution
`truncate`	Truncate probability distribution object
`var`	Variance of probability distribution

`gevcdf`	Generalized extreme value cumulative distribution function
`gevpdf`	Generalized extreme value probability density function
`gevinv`	Generalized extreme value inverse cumulative distribution function
`gevlike`	Generalized extreme value negative log-likelihood
`gevstat`	Generalized extreme value mean and variance
`gevfit`	Generalized extreme value parameter estimates
`gevrnd`	Generalized extreme value random numbers

Objects

GeneralizedExtremeValueDistribution Generalized extreme value probability distribution object

Topics

Generalized Extreme Value Distribution

The generalized extreme value distribution is often used to model the smallest or largest value among a large set of independent, identically distributed random values representing measurements or observations.

Modelling Data with the Generalized Extreme Value Distribution

This example shows how to fit the generalized extreme value distribution using maximum likelihood estimation.

Statistics and Machine Learning Toolbox Documentation

Support

Mastering Machine Learning: A Step-by-Step Guide with MATLAB

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