Package dev.nm.stat.evt.evd.univariate.fitting.acer

Class ACERFunction

java.lang.Object
dev.nm.analysis.function.rn2r1.AbstractRealScalarFunction
dev.nm.analysis.function.rn2r1.univariate.AbstractUnivariateRealFunction
dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction
All Implemented Interfaces:
Function<Vector,Double>, RealScalarFunction, UnivariateRealFunction
public class ACERFunction extends AbstractUnivariateRealFunction
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.

The function is of the form (Gumbel-type): \[ \hat{\epsilon_k}(\eta) = q_k exp(-a_k (\eta - b_k)^{c_k}), \eta \ge \eta_1 \]

The R equivalent function is acer::acer.evaluate. Note: R defines the conditional of epsilon using "less than" but this implementation sticks to the original paper which uses "less than or equal to".

  • Constructor Details

  • Method Details

    • evaluate

      public double evaluate(double eta)
      Compute the epsilon value.
      Parameters:
      eta - the barrier level \(\eta\)
      Returns:
      the epsilon