com.imsl.stat.distributions

Class LogisticPD

java.lang.Object

com.imsl.stat.distributions.ProbabilityDistribution

com.imsl.stat.distributions.LogisticPD

All Implemented Interfaces:

PDFGradientInterface, PDFHessianInterface, Serializable, Cloneable

public class LogisticPD
extends ProbabilityDistribution
implements Serializable, Cloneable, PDFHessianInterface

The logistic probability distribution.

See Also:

Example, Serialized Form

Constructor Summary

Constructors

Constructor and Description
LogisticPD() Constructor for the logistic probability distribution.

Method Summary

Modifier and Type	Method and Description
double[]	getMethodOfMomentsEstimates(double[] x) Returns the method-of-moments estimates given the sample data.
double[]	getParameterLowerBounds() Returns the lower bounds of the parameters.
double[]	getParameterUpperBounds() Returns the upper bounds of the parameters.
double[]	getPDFGradient(double x, double... params) Returns the analytic gradient of the pdf.
double[][]	getPDFHessian(double x, double... params) Returns the analytic Hessian of the pdf.
double	pdf(double x, double... params) Returns the value of the logistic probability density function.

Methods inherited from class com.imsl.stat.distributions.ProbabilityDistribution

getNumberOfParameters, getPDFGradientApproximation, getPDFHessianApproximation, getRangeOfX, setRangeOfX

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

LogisticPD

public LogisticPD()

Constructor for the logistic probability distribution.

Method Detail

getParameterLowerBounds

public double[] getParameterLowerBounds()

Returns the lower bounds of the parameters.

Specified by:

getParameterLowerBounds in class ProbabilityDistribution

Returns:

a double array of length 2 containing the lower bounds for $\mu\in\mathbb{R}$ and $\sigma\gt0$

getParameterUpperBounds

public double[] getParameterUpperBounds()

Returns the upper bounds of the parameters.

Specified by:

getParameterUpperBounds in class ProbabilityDistribution

Returns:

a double array of length 2 containing the upper bounds for $\mu\in\mathbb{R}$ and $\sigma\gt0$

pdf

public double pdf(double x,
                  double... params)

Returns the value of the logistic probability density function.

The probability density function of the logistic distribution is

$$f(x,\mu,\sigma)=\frac{e^{-(x-\mu)/\sigma}} {\sigma\left (1+e^{-(x-\mu)/\sigma} \right )^{2}}$$

where $\mu$ is the location parameter and $\sigma \gt 0$ is the scale parameter.

Specified by:

pdf in class ProbabilityDistribution

Parameters:

x - a double, the value (quantile) at which to evaluate the pdf

params - a double array containing the location and scale parameters. The parameters can also be given in the form pdf(x,a,b), where a=$\mu$ and b=$\sigma$ are scalars.

Returns:

a double, the value of the probability density function evaluated at x given the parameters

getPDFGradient

public double[] getPDFGradient(double x,
                               double... params)

Returns the analytic gradient of the pdf.

Specified by:

getPDFGradient in interface PDFGradientInterface

Parameters:

x - a double, the value at which to evaluate the gradient

params - a double array containing the parameters

Returns:

a double array containing the first partial derivatives of the pdf with respect to the parameters

getPDFHessian

public double[][] getPDFHessian(double x,
                                double... params)

Returns the analytic Hessian of the pdf.

Specified by:

getPDFHessian in interface PDFHessianInterface

Parameters:

x - a double, the value at which to evaluate the Hessian

params - a double array containing the parameters

Returns:

a double matrix containing the second partial derivatives of the pdf with respect to the parameters

getMethodOfMomentsEstimates

public double[] getMethodOfMomentsEstimates(double[] x)

Returns the method-of-moments estimates given the sample data.

Parameters:

x - a double array containing the data

Returns:

a double array containing method-of-moments estimates for $\mu$ and $\sigma$, the parameters of the logistic distribution