| ACERAnalysis |
Average Conditional Exceedance Rate (ACER) method is for estimating the cdf of the maxima \(M\)
distribution from observations.
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| ACERAnalysis.Result |
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| 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.
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| 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.
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| ACERFunction.ACERParameter |
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| ACERInverseFunction |
The inverse of the ACER function.
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| ACERLogFunction |
The ACER function in log scale (base e), i.e., \(log(\epsilon_k(\eta))\).
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| ACERReturnLevel |
Given an ACER function, compute the return level \(\eta\) for a given return period \(R\).
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| LinearFit |
Find the parameters for the ACER function from the given empirical epsilon, using OLS regression
on the logarithm of the values.
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| NonlinearFit |
Fit log-ACER function by sequential quadratic programming (SQP) minimization (of weighted RSS),
using LinearFit's solution as the initial guess.
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| NonlinearFit.Result |
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