com.opengamma.analytics.math.statistics.estimation.StudentTDistributionMaximumLikelihoodEstimator.java Source code

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Here is the source code for com.opengamma.analytics.math.statistics.estimation.StudentTDistributionMaximumLikelihoodEstimator.java

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/**
 * Copyright (C) 2009 - present by OpenGamma Inc. and the OpenGamma group of companies
 * 
 * Please see distribution for license.
 */
package com.opengamma.analytics.math.statistics.estimation;

import org.apache.commons.lang.Validate;

import com.opengamma.analytics.math.function.Function1D;
import com.opengamma.analytics.math.function.special.GammaFunction;
import com.opengamma.analytics.math.minimization.GoldenSectionMinimizer1D;
import com.opengamma.analytics.math.minimization.ScalarMinimizer;
import com.opengamma.analytics.math.statistics.descriptive.MeanCalculator;
import com.opengamma.analytics.math.statistics.descriptive.PopulationStandardDeviationCalculator;
import com.opengamma.analytics.math.statistics.distribution.ProbabilityDistribution;
import com.opengamma.analytics.math.statistics.distribution.StudentTDistribution;
import com.opengamma.util.ArgumentChecker;

/**
 * 
 */
public class StudentTDistributionMaximumLikelihoodEstimator extends DistributionParameterEstimator<Double> {
    // TODO add error estimates
    private final ScalarMinimizer _minimizer = new GoldenSectionMinimizer1D();
    private final Function1D<Double, Double> _gamma = new GammaFunction();
    private final Function1D<double[], Double> _mean = new MeanCalculator();
    private final Function1D<double[], Double> _std = new PopulationStandardDeviationCalculator();

    @Override
    public ProbabilityDistribution<Double> evaluate(final double[] x) {
        Validate.notNull(x, "x");
        ArgumentChecker.notEmpty(x, "x");
        final double[] standardized = getStandardizedData(x);
        final Function1D<Double, Double> f = new Function1D<Double, Double>() {

            @SuppressWarnings("synthetic-access")
            @Override
            public Double evaluate(final Double nu) {
                double sum = 0;
                for (final double t : standardized) {
                    sum += Math.log(_gamma.evaluate((nu + 1) / 2.) * Math.pow(1 + t * t / (nu - 2), -(nu + 1) / 2.)
                            / Math.sqrt(Math.PI * (nu - 2)) / _gamma.evaluate(nu / 2.));
                }
                return -sum;
            }

        };
        return new StudentTDistribution(_minimizer.minimize(f, 0.0, 3., 10.));
    }

    protected double[] getStandardizedData(final double[] x) {
        final double mean = _mean.evaluate(x);
        final double std = _std.evaluate(x);
        final double[] z = new double[x.length];
        for (int i = 0; i < x.length; i++) {
            z[i] = (x[i] - mean) / std;
        }
        return z;
    }
}