**'edu.rit.numeric.RobustFit'**Java class

## Class RobustFit

- java.lang.Object
- edu.rit.numeric.RobustFit

public class RobustFitextends Object

Class RobustFit uses a robust estimation procedure to fit a series of (*x,y*) data points to a model. The data series is an instance of class XYSeries. The model is represented by a ParameterizedFunction that computes the*y*value, given an*x*value. The model also has*parameters*.Given a data series, a model function, and an initial guess for the parameter values, class RobustFit's

`fit()`method finds parameter values that minimize the following*metric:***Σ**_{i}*ρ*(*y*_{i}−*f*(*x*_{i}, parameters))where

*f*is the model function and*ρ*is one of these metric functions:- Normal:
*ρ*(*z*) =*z*^{2}/2 - Exponential:
*ρ*(*z*) = |*z*| - Cauchy (default):
*ρ*(*z*) = log (1 +*z*^{2}/2)

In other words, the

`fit()`method*fits*the model to the data by adjusting the parameters to minimize the metric.The metric function is the negative logarithm of the probability distribution of the errors in the

*y*values. The above metric functions correspond to normal, two-sided exponential, and Cauchy error distributions.The metric functions differ in how they treat

*outliers,*i.e., data points that deviate from the model. The normal metric function gives increasing weights to points with increasing deviations. However, because of the increasing weights, outlier points may skew the fit (hence, this is not really a "robust" metric function). The exponential metric function gives equal weights to all points, regardless of deviation. This reduces the influence of outliers on the fit, yielding a more robust fit. With the Cauchy metric function, the weights first increase, then decrease as the deviations increase. This reduces the influence of outliers even further.The

`fit()`method uses class MDMinimizationDownhillSimplex to find the parameter values that minimize the metric. The inputs to and outputs from the`fit()`method are stored in fields of an instance of class RobustFit.The

`fitWithDistribution()`method uses the*bootstrapping*technique to determine the distribution of the model parameters, which depends on the error distribution of the data points. Bootstrapping performs multiple iterations of the model fitting procedure. On each iteration, a trial data set the same size as the original data set is created by sampling the original data points with replacement, and model parameters for the trial data set are computed. The`fitWithDistribution()`method outputs a series of the parameter values found at each iteration; the confidence region for the parameters; and the goodness-of-fit*p*-value.- Normal:

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