The SEVERITY procedure estimates parameters of any probability distribution that is used to model the magnitude (severity) of a continuous-valued event of interest. The SEVERITY procedure includes the following features:
parameter estimation of predefined distribution models, including the following:
Burr distribution
exponential distribution
gamma distribution
generalized Pareto distribution
inverse Gaussian (Wald) distribution
lognormal distribution
Pareto distribution
Tweedie distribution
Weibull distribution
parameter estimation of arbitrarily defined parametric distribution models
fitting distributions to data by either truncation or censoring
group estimation
several fit statistics, including the following:
log likelihood
Akaike’s information criterion (AIC)
corrected Akaike’s information criterion (AICC)
Schwarz Bayesian information criterion (BIC)
Kolmogorov-Smirnov statistic (KS)
Anderson-Darling statistic (AD)
Cramér–von Mises statistic (CvM)
regression effects
scoring functions
multithreaded computation
ability to specify the objective function for optimization
plots of the estimated cumulative distribution function (CDF), the estimated empirical distribution function (EDF), and the estimated probability density function (PDF)