Package | Description |
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dev.nm.stat.evt.evd.univariate.fitting.acer |
Modifier and Type | Method and Description |
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ACERFunction.ACERParameter |
LinearFit.fit(double[] eta,
double[] epsilon,
double peakMean)
Fit the ACER function with OLS.
|
ACERFunction.ACERParameter |
ACERConfidenceInterval.getLowerParameter() |
ACERFunction.ACERParameter |
NonlinearFit.Result.getParameter() |
ACERFunction.ACERParameter |
ACERConfidenceInterval.getUpperParameter() |
Modifier and Type | Method and Description |
---|---|
static double |
NonlinearFit.computeWeightedRSS(ACERFunction.ACERParameter param,
double[] barrierLevels,
double[] epsilons,
double[] weights)
Measure how fit the estimated log-ACER function to the empirical epsilons by weighted sum of
squared residuals (RSS).
|
NonlinearFit.Result |
NonlinearFit.fitWithWeightsAndInitial(double[] eta,
double[] epsilon,
double[] weights,
ACERFunction.ACERParameter initial,
double minLevel,
double tailMarker) |
Constructor and Description |
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ACERConfidenceInterval(ACERFunction.ACERParameter parameter,
EmpiricalACER estimates,
double N,
double tailMarker) |
ACERFunction(ACERFunction.ACERParameter param) |
ACERInverseFunction(ACERFunction.ACERParameter param) |
ACERLogFunction(ACERFunction.ACERParameter param)
Create an instance with the ACER function parameter.
|
ACERReturnLevel(ACERFunction.ACERParameter parameter,
double N)
Create an instance with the (estimated) ACER function parameter and the total number of
events.
|
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