# Package com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer

• Class Summary
Class Description
ACERAnalysis
Average Conditional Exceedance Rate (ACER) method is for estimating the cdf of the maxima $$M$$ distribution from observations.
ACERAnalysis.Result
ACERConfidenceInterval
Using the given (estimated) ACER function as the mean, find the ACER parameters at the lower and upper bounds of the estimated confidence interval of ACER values.
ACERFunction
The ACER (Average Conditional Exceedance Rate) function $$\epsilon_k(\eta)$$ approximates the probability $\epsilon_k(\eta) = Pr(X_k > \eta | X_1 \le \eta, X_2 \le \eta, ..., X_{k-1} \le \eta)$ for a sequence of stochastic process observations $$X_i$$ with a k-step memory.
ACERFunction.ACERParameter
ACERInverseFunction
The inverse of the ACER function.
ACERLogFunction
The ACER function in log scale (base e), i.e., $$log(\epsilon_k(\eta))$$.
ACERReturnLevel
Given an ACER function, compute the return level $$\eta$$ for a given return period $$R$$.
LinearFit
Find the parameters for the ACER function from the given empirical epsilon, using OLS regression on the logarithm of the values.
NonlinearFit
Fit log-ACER function by sequential quadratic programming (SQP) minimization (of weighted RSS), using LinearFit's solution as the initial guess.
NonlinearFit.Result