public class NonlinearFit extends Object
LinearFit's solution as the initial guess. The weights are computed as:
\[
w_i = \frac{1}{(\log CI^+(\eta_i) - \log CI^-(\eta_i))^2}
\]
These weights measure the reciprocals of variances and are used to put less emphasis on the more
uncertain values for the ACER function.| Modifier and Type | Class and Description |
|---|---|
static class |
NonlinearFit.Result |
| Constructor and Description |
|---|
NonlinearFit() |
| Modifier and Type | Method and Description |
|---|---|
static double |
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).
|
static double[] |
computeWeights(double[] epsilon,
double[] confint)
Compute weights from epsilon values and their corresponding confidence interval half-width.
|
NonlinearFit.Result |
fit(double[] eta,
double[] epsilon,
double[] confWidth,
double peakMean,
double tailMarker)
Fit the ACER function with the input values of barrier levels, epsilons, confidence
intervals, and the mean of the peaks.
|
NonlinearFit.Result |
fit(EmpiricalACER estimates,
double tailMarker)
Fit ACER function with empirical ACER estimates.
|
NonlinearFit.Result |
fitWithWeights(double[] eta,
double[] epsilon,
double[] weights,
double peakMean,
double minLevel,
double tailMarker)
Fit the ACER function with the input values of barrier levels, epsilons, confidence
intervals, and the mean of the peaks.
|
NonlinearFit.Result |
fitWithWeightsAndInitial(double[] eta,
double[] epsilon,
double[] weights,
ACERFunction.ACERParameter initial,
double minLevel,
double tailMarker) |
public NonlinearFit.Result fit(EmpiricalACER estimates, double tailMarker)
estimates - the empirical ACER estimatestailMarker - the tail markerpublic NonlinearFit.Result fit(double[] eta, double[] epsilon, double[] confWidth, double peakMean, double tailMarker)
eta - the barrier levelsepsilon - the ACER valuesconfWidth - the confidence interval half-widths for the ACER valuespeakMean - the mean of the events (or peaks)tailMarker - the tail markerpublic NonlinearFit.Result fitWithWeights(double[] eta, double[] epsilon, double[] weights, double peakMean, double minLevel, double tailMarker)
eta - the barrier levelsepsilon - the ACER values (assume all positive as ACER represents a probability)weights - the weighting for the ACER values (put more emphasis on more confident
values)peakMean - the mean of the events (or peaks)minLevel - the minimum barrier leveltailMarker - the tail markerpublic NonlinearFit.Result fitWithWeightsAndInitial(double[] eta, double[] epsilon, double[] weights, ACERFunction.ACERParameter initial, double minLevel, double tailMarker)
public static double[] computeWeights(double[] epsilon,
double[] confint)
epsilon - the epsilon valuesconfint - the confidence interval half-widthpublic static double computeWeightedRSS(ACERFunction.ACERParameter param, double[] barrierLevels, double[] epsilons, double[] weights)
param - the estimated parameter for ACER functionbarrierLevels - the barrier levelsepsilons - the (positive) empirical epsilons for the corresponding barrier levelsweights - the weights for the corresponding epsilonCopyright © 2010-2020 NM FinTech Ltd.. All Rights Reserved.