umontreal.iro.lecuyer.probdist
Class WeibullDist

java.lang.Object
  umontreal.iro.lecuyer.probdist.ContinuousDistribution
      umontreal.iro.lecuyer.probdist.WeibullDist

All Implemented Interfaces:

Distribution

public class WeibullDist
extends ContinuousDistribution

This class extends the class ContinuousDistribution for the Weibull distribution with shape parameter α > 0, location parameter δ, and scale parameter λ > 0. The density function is

f (x) = αλ^α(x - δ)^α-1e^{-(λ(x-δ))α} for x > δ.

the distribution function is

F(x) = 1 - e^{-(λ(x-δ))α} for x > δ,

and the inverse distribution function is

F^-1(u) = (- ln(1 - u))^1/α/λ + δ for 0 <= u < 1.

Field Summary

Fields inherited from class umontreal.iro.lecuyer.probdist.ContinuousDistribution

decPrec

Constructor Summary

WeibullDist(double alpha)
Constructs a WeibullDist object with parameters α = alpha, λ = 1, and δ = 0.

WeibullDist(double alpha, double lambda, double delta)
Constructs a WeibullDist object with parameters α = alpha, λ = lambda, and δ = delta.

Method Summary

double	barF(double x) Returns the complementary distribution function.
static double	barF(double alpha, double x) Same as barF (alpha, 1, 0, x).
static double	barF(double alpha, double lambda, double delta, double x) Computes the complementary distribution function.
double	cdf(double x) Returns the distribution function F(x).
static double	cdf(double alpha, double x) Same as cdf (alpha, 1, 0, x).
static double	cdf(double alpha, double lambda, double delta, double x) Computes the distribution function.
double	density(double x) Returns f (x), the density evaluated at x.
static double	density(double alpha, double x) Same as density (alpha, 1, 0, x).
static double	density(double alpha, double lambda, double delta, double x) Computes the density function.
double	getAlpha() Returns the parameter α.
double	getDelta() Returns the parameter δ.
static WeibullDist	getInstanceFromMLE(double[] x, int n) Creates a new instance of a Weibull distribution with parameters α, λ and δ = 0 estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.
double	getLambda() Returns the parameter λ.
double	getMean() Returns the mean.
static double	getMean(double alpha, double lambda, double delta) Computes and returns the mean of the Weibull distribution with parameters α, λ and δ.
static double[]	getMLE(double[] x, int n) Estimates the parameters (α, λ) of the Weibull distribution, assuming that δ = 0, using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.
double[]	getParams() Return a table containing the parameters of the current distribution.
double	getStandardDeviation() Returns the standard deviation.
static double	getStandardDeviation(double alpha, double lambda, double delta) Computes and returns the standard deviation of the Weibull distribution with parameters α, λ and δ.
double	getVariance() Returns the variance.
static double	getVariance(double alpha, double lambda, double delta) Computes and returns the variance of the Weibull distribution with parameters α, λ and δ.
double	inverseF(double u) Returns the inverse distribution function x = F-1(u).
static double	inverseF(double alpha, double x) Same as inverseF (alpha, 1, 0, x).
static double	inverseF(double alpha, double lambda, double delta, double u) Computes the inverse of the distribution function.
void	setParams(double alpha, double lambda, double delta) Sets the parameters α, λ and δ for this object.
String	toString()

Methods inherited from class umontreal.iro.lecuyer.probdist.ContinuousDistribution

getXinf, getXsup, inverseBisection, inverseBrent, setXinf, setXsup

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

WeibullDist

public WeibullDist(double alpha)

Constructs a WeibullDist object with parameters α = alpha, λ = 1, and δ = 0.

WeibullDist

public WeibullDist(double alpha,
                   double lambda,
                   double delta)

Constructs a WeibullDist object with parameters α = alpha, λ = lambda, and δ = delta.

Method Detail

density

public double density(double x)

Description copied from class: ContinuousDistribution

Returns f (x), the density evaluated at x.

Specified by:

density in class ContinuousDistribution

Parameters:

x - value at which the density is evaluated

Returns:

density function evaluated at x

cdf

public double cdf(double x)

Description copied from interface: Distribution

Returns the distribution function F(x).

Parameters:

x - value at which the distribution function is evaluated

Returns:

distribution function evaluated at x

barF

public double barF(double x)

Description copied from class: ContinuousDistribution

Returns the complementary distribution function. The default implementation computes bar(F)(x) = 1 - F(x).

Specified by:

barF in interface Distribution

Overrides:

barF in class ContinuousDistribution

Parameters:

x - value at which the complementary distribution function is evaluated

Returns:

complementary distribution function evaluated at x

inverseF

public double inverseF(double u)

Description copied from class: ContinuousDistribution

Returns the inverse distribution function x = F^-1(u). Restrictions: u∈[0, 1].

Specified by:

inverseF in interface Distribution

Overrides:

inverseF in class ContinuousDistribution

Parameters:

u - value at which the inverse distribution function is evaluated

Returns:

the inverse distribution function evaluated at u

getMean

public double getMean()

Description copied from class: ContinuousDistribution

Returns the mean.

Specified by:

getMean in interface Distribution

Overrides:

getMean in class ContinuousDistribution

Returns:

the mean

getVariance

public double getVariance()

Description copied from class: ContinuousDistribution

Returns the variance.

Specified by:

getVariance in interface Distribution

Overrides:

getVariance in class ContinuousDistribution

Returns:

the variance

getStandardDeviation

public double getStandardDeviation()

Description copied from class: ContinuousDistribution

Returns the standard deviation.

Specified by:

getStandardDeviation in interface Distribution

Overrides:

getStandardDeviation in class ContinuousDistribution

Returns:

the standard deviation

density

public static double density(double alpha,
                             double lambda,
                             double delta,
                             double x)

Computes the density function.

density

public static double density(double alpha,
                             double x)

Same as density (alpha, 1, 0, x).

cdf

public static double cdf(double alpha,
                         double lambda,
                         double delta,
                         double x)

Computes the distribution function.

cdf

public static double cdf(double alpha,
                         double x)

Same as cdf (alpha, 1, 0, x).

barF

public static double barF(double alpha,
                          double lambda,
                          double delta,
                          double x)

Computes the complementary distribution function.

barF

public static double barF(double alpha,
                          double x)

Same as barF (alpha, 1, 0, x).

inverseF

public static double inverseF(double alpha,
                              double lambda,
                              double delta,
                              double u)

Computes the inverse of the distribution function.

inverseF

public static double inverseF(double alpha,
                              double x)

Same as inverseF (alpha, 1, 0, x).

getMLE

public static double[] getMLE(double[] x,
                              int n)

Estimates the parameters (α, λ) of the Weibull distribution, assuming that δ = 0, using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1. The estimates are returned in a two-element array, in regular order: [α, λ].

Parameters:

x - the list of observations to use to evaluate parameters

n - the number of observations to use to evaluate parameters

Returns:

returns the parameter [ hat(α), hat(λ), hat(δ) = 0]

getInstanceFromMLE

public static WeibullDist getInstanceFromMLE(double[] x,
                                             int n)

Creates a new instance of a Weibull distribution with parameters α, λ and δ = 0 estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

Parameters:

x - the list of observations to use to evaluate parameters

n - the number of observations to use to evaluate parameters

getMean

public static double getMean(double alpha,
                             double lambda,
                             double delta)

Computes and returns the mean of the Weibull distribution with parameters α, λ and δ.

Returns:

the mean of the Weibull distribution E[X] = δ + Γ(1 + 1/α)/λ

getVariance

public static double getVariance(double alpha,
                                 double lambda,
                                 double delta)

Computes and returns the variance of the Weibull distribution with parameters α, λ and δ.

Returns:

the variance of the Weibull distribution Var[X] = 1/λ²| Γ(2/α +1) - Γ²(1/α + 1)|

getStandardDeviation

public static double getStandardDeviation(double alpha,
                                          double lambda,
                                          double delta)

Computes and returns the standard deviation of the Weibull distribution with parameters α, λ and δ.

Returns:

the standard deviation of the Weibull distribution

getAlpha

public double getAlpha()

Returns the parameter α.

getLambda

public double getLambda()

Returns the parameter λ.

getDelta

public double getDelta()

Returns the parameter δ.

setParams

public void setParams(double alpha,
                      double lambda,
                      double delta)

Sets the parameters α, λ and δ for this object.

getParams

public double[] getParams()

Return a table containing the parameters of the current distribution. This table is put in regular order: [α, λ, δ].

toString

public String toString()

Overrides:

toString in class Object