Extends the class ContinuousDistribution for the lognormal distribution [99] . More...

Inheritance diagram for LognormalDist:

Collaboration diagram for LognormalDist:

Public Member Functions
	LognormalDist ()
	Constructs a `LognormalDist` object with default parameters \(\mu= 0\) and \(\sigma= 1\).

	LognormalDist (double mu, double sigma)
	Constructs a `LognormalDist` object with parameters \(\mu\) = `mu` and \(\sigma\) = `sigma`.

double	density (double x)

double	cdf (double x)
	Returns the distribution function \(F(x)\). More...

double	barF (double x)
	Returns \(\bar{F}(x) = 1 - F(x)\). More...

double	inverseF (double u)
	Returns the inverse distribution function \(F^{-1}(u)\), defined in ( inverseF ). More...

double	getMean ()
	Returns the mean of the distribution function.

double	getVariance ()
	Returns the variance of the distribution function.

double	getStandardDeviation ()
	Returns the standard deviation of the distribution function.

double	getMu ()
	Returns the parameter \(\mu\) of this object.

double	getSigma ()
	Returns the parameter \(\sigma\) of this object.

void	setParams (double mu, double sigma)
	Sets the parameters \(\mu\) and \(\sigma\) of this object.

double []	getParams ()
	Returns a table containing the parameters of the current distribution, in the order: [ \(\mu\), \(\sigma\)].

String	toString ()
	Returns a `String` containing information about the current distribution.

Public Member Functions inherited from ContinuousDistribution
abstract double	density (double x)
	Returns \(f(x)\), the density evaluated at \(x\). More...

double	barF (double x)
	Returns the complementary distribution function. More...

double	inverseBrent (double a, double b, double u, double tol)
	Computes the inverse distribution function \(x = F^{-1}(u)\), using the Brent-Dekker method. More...

double	inverseBisection (double u)
	Computes and returns the inverse distribution function \(x = F^{-1}(u)\), using bisection. More...

double	inverseF (double u)
	Returns the inverse distribution function \(x = F^{-1}(u)\). More...

double	getMean ()
	Returns the mean. More...

double	getVariance ()
	Returns the variance. More...

double	getStandardDeviation ()
	Returns the standard deviation. More...

double	getXinf ()
	Returns \(x_a\) such that the probability density is 0 everywhere outside the interval \([x_a, x_b]\). More...

double	getXsup ()
	Returns \(x_b\) such that the probability density is 0 everywhere outside the interval \([x_a, x_b]\). More...

void	setXinf (double xa)
	Sets the value \(x_a=\) `xa`, such that the probability density is 0 everywhere outside the interval \([x_a, x_b]\). More...

void	setXsup (double xb)
	Sets the value \(x_b=\) `xb`, such that the probability density is 0 everywhere outside the interval \([x_a, x_b]\). More...

Static Public Member Functions
static double	density (double mu, double sigma, double x)
	Computes the lognormal density function \(f(x)\) in ( flognormal ).

static double	cdf (double mu, double sigma, double x)
	Computes the lognormal distribution function, using NormalDist.cdf01.

static double	barF (double mu, double sigma, double x)
	Computes the lognormal complementary distribution function \(\bar{F}(x)\), using NormalDist.barF01.

static double	inverseF (double mu, double sigma, double u)
	Computes the inverse of the lognormal distribution function, using NormalDist.inverseF01.

static double []	getMLE (double[] x, int n)
	Estimates the parameters \((\mu, \sigma)\) of the lognormal distribution using the maximum likelihood method, from the \(n\) observations \(x[i]\), \(i = 0, 1,…, n-1\). More...

static LognormalDist	getInstanceFromMLE (double[] x, int n)
	Creates a new instance of a lognormal distribution with parameters \(\mu\) and \(\sigma\) estimated using the maximum likelihood method based on the \(n\) observations \(x[i]\), \(i = 0, 1, …, n-1\). More...

static double	getMean (double mu, double sigma)
	Computes and returns the mean \(E[X] = e^{\mu+ \sigma^2/2}\) of the lognormal distribution with parameters \(\mu\) and \(\sigma\). More...

static double	getVariance (double mu, double sigma)
	Computes and returns the variance \(\mbox{Var}[X] = e^{2\mu+ \sigma^2}(e^{\sigma^2} - 1)\) of the lognormal distribution with parameters \(\mu\) and \(\sigma\). More...

static double	getStandardDeviation (double mu, double sigma)
	Computes and returns the standard deviation of the lognormal distribution with parameters \(\mu\) and \(\sigma\). More...

Additional Inherited Members
Public Attributes inherited from ContinuousDistribution
int	decPrec = 15

Protected Attributes inherited from ContinuousDistribution
double	supportA = Double.NEGATIVE_INFINITY

double	supportB = Double.POSITIVE_INFINITY

Static Protected Attributes inherited from ContinuousDistribution
static final double	XBIG = 100.0

static final double	XBIGM = 1000.0

static final double []	EPSARRAY

Detailed Description

Extends the class ContinuousDistribution for the lognormal distribution [99] .

(See also the Johnson \(S_L\) distribution JohnsonSLDist in this package.) It has scale parameter \(\mu\) and shape parameter \(\sigma> 0\). The density is

\[ f(x) = \frac{1}{\sqrt{2\pi}\sigma x} e^{-(\ln(x) - \mu)^2/(2\sigma^2)} \qquad\mbox{for } x>0, \tag{flognormal} \]

and 0 elsewhere. The distribution function is

\[ F(x) = \Phi\left({(\ln(x) - \mu)/\sigma}\right) \qquad\mbox{for } x>0, \]

where \(\Phi\) is the standard normal distribution function. Its inverse is given by

\[ F^{-1}(u) = e^{\mu+ \sigma\Phi^{-1} (u)} \qquad\mbox{for } 0 \le u < 1. \]

If \(\ln(Y)\) has a normal distribution, then \(Y\) has a lognormal distribution with the same parameters.

This class relies on the methods NormalDist.cdf01 and NormalDist.inverseF01 of NormalDist to approximate \(\Phi\) and \(\Phi^{-1}\).

Member Function Documentation

◆ barF()

double barF ( double x )

Returns \(\bar{F}(x) = 1 - F(x)\).

Parameters

x	value at which the complementary distribution function is evaluated

Returns: complementary distribution function evaluated at x

Implements Distribution.

◆ cdf()

double cdf ( double x )

Returns the distribution function \(F(x)\).

Parameters

x	value at which the distribution function is evaluated

Returns: distribution function evaluated at x

Implements Distribution.

◆ getInstanceFromMLE()

static LognormalDist getInstanceFromMLE	(	double []	x,
		int	n
	)

static

Creates a new instance of a lognormal distribution with parameters \(\mu\) and \(\sigma\) 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()

static double getMean	(	double	mu,
		double	sigma
	)

static

Computes and returns the mean \(E[X] = e^{\mu+ \sigma^2/2}\) of the lognormal distribution with parameters \(\mu\) and \(\sigma\).

Returns: the mean of the lognormal distribution

◆ getMLE()

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

static

Estimates the parameters \((\mu, \sigma)\) of the lognormal distribution 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: [ \(\mu\), \(\sigma\)]. The maximum likelihood estimators are the values \((\hat{\mu}, \hat{\sigma})\) that satisfy the equations:

\begin{align*} \hat{\mu} & = \frac{1}{n} \sum_{i=1}^n \ln(x_i) \\ \hat{\sigma} & = \sqrt{\frac{1}{n} \sum_{i=1}^n (\ln(x_i) - \hat{\mu})^2}. \end{align*}

See [99] (page 220).

Parameters

x	the list of observations used to evaluate parameters
n	the number of observations used to evaluate parameters

Returns: returns the parameters [ \(\hat{\mu}\), \(\hat{\sigma}\)]

◆ getStandardDeviation()

static double getStandardDeviation	(	double	mu,
		double	sigma
	)

static

Computes and returns the standard deviation of the lognormal distribution with parameters \(\mu\) and \(\sigma\).

Returns: the standard deviation of the lognormal distribution

◆ getVariance()

static double getVariance	(	double	mu,
		double	sigma
	)

static

Computes and returns the variance \(\mbox{Var}[X] = e^{2\mu+ \sigma^2}(e^{\sigma^2} - 1)\) of the lognormal distribution with parameters \(\mu\) and \(\sigma\).

Returns: the variance of the lognormal distribution

◆ inverseF()

double inverseF ( double u )

Returns the inverse distribution function \(F^{-1}(u)\), defined in ( inverseF ).

Parameters

u	value in the interval \((0,1)\) for which the inverse distribution function is evaluated

Returns: the inverse distribution function evaluated at u

Implements Distribution.

The documentation for this class was generated from the following file:

LognormalDist.java

Public Member Functions

Static Public Member Functions

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ barF()

◆ cdf()

◆ getInstanceFromMLE()

◆ getMean()

◆ getMLE()

◆ getStandardDeviation()

◆ getVariance()

◆ inverseF()