Estimate the cumulative distribution function (CDF) from data in a non-parametric or semi-parametric fashion. It also illustrates the inversion method for generating random numbers
Use some more advanced techniques with the Statistics and Machine Learning Toolbox™ function mle to fit custom distributions to univariate data. The techniques include fitting models to
Fit the generalized extreme value distribution using maximum likelihood estimation. The extreme value distribution is used to model the largest or smallest value from a group or block of
The difference between fitting a curve to a set of points, and fitting a probability distribution to a sample of data.
Use the Statistics and Machine Learning Toolbox™ function mle to fit custom distributions to univariate data.
Analyze lifetime data with censoring. In biological or medical applications, this is known as survival analysis, and the times may represent the survival time of an organism or the time
Fit tail data to the Generalized Pareto distribution by maximum likelihood estimation.