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java.lang.Objectumontreal.iro.lecuyer.probdist.ContinuousDistribution
umontreal.iro.lecuyer.probdist.LoglogisticDist
public class LoglogisticDist
Extends the class ContinuousDistribution
for the
Log-Logistic distribution with shape parameter
α > 0
and scale parameter β > 0.
Its density is
Field Summary |
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Fields inherited from class umontreal.iro.lecuyer.probdist.ContinuousDistribution |
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decPrec |
Constructor Summary | |
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LoglogisticDist(double alpha,
double beta)
Constructs a log-logistic distribution with parameters α and β. |
Method Summary | |
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double |
barF(double x)
Returns bar(F)(x) = 1 - F(x). |
static double |
barF(double alpha,
double beta,
double x)
Computes the complementary distribution function of the log-logistic distribution with parameters α and β. |
double |
cdf(double x)
Computes and returns the distribution function F(x). |
static double |
cdf(double alpha,
double beta,
double x)
Computes the distribution function of the log-logistic distribution with parameters α and β. |
double |
density(double x)
Returns f (x), the density of X evaluated at x. |
static double |
density(double alpha,
double beta,
double x)
Computes the density function for a log-logisitic distribution with parameters α and β. |
double |
getAlpha()
Return the parameter α of this object. |
double |
getBeta()
Returns the parameter β of this object. |
static LoglogisticDist |
getInstanceFromMLE(double[] x,
int n)
Creates a new instance of a log-logistic distribution with parameters α and β estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1. |
static double[] |
getMaximumLikelihoodEstimate(double[] x,
int n)
Estimates and returns the parameters [ hat(α), hat(β)] of the log-logistic distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1. |
double |
getMean()
Returns the mean of the distribution function. |
static double |
getMean(double alpha,
double beta)
Computes and returns the mean of the log-logistic distribution with parameters α and β. |
double |
getStandardDeviation()
Returns the standard deviation of the distribution function. |
static double |
getStandardDeviation(double alpha,
double beta)
Computes and returns the standard deviation of the log-logistic distribution with parameters α and β. |
double |
getVariance()
Returns the variance of the distribution function. |
static double |
getVariance(double alpha,
double beta)
Computes and returns the variance of the log-logistic distribution with parameters α and β. |
double |
inverseF(double u)
Computes and returns the inverse distribution function F-1(u), defined in. |
static double |
inverseF(double alpha,
double beta,
double u)
Computes the inverse of the log-logistic distribution with parameters α and β. |
void |
setParams(double alpha,
double beta)
Sets the parameters α and β of this object. |
Methods inherited from class umontreal.iro.lecuyer.probdist.ContinuousDistribution |
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inverseBisection, inverseBrent |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public LoglogisticDist(double alpha, double beta)
Method Detail |
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public double density(double x)
ContinuousDistribution
density
in class ContinuousDistribution
x
- value at which the density is evaluated
public double cdf(double x)
Distribution
x
- value at which the distribution function is evaluated
public double barF(double x)
Distribution
barF
in interface Distribution
barF
in class ContinuousDistribution
x
- value at which the complementary distribution function is evaluated
public double inverseF(double u)
Distribution
inverseF
in interface Distribution
inverseF
in class ContinuousDistribution
u
- value in the interval (0, 1) for which the inverse
distribution function is evaluated
public double getMean()
Distribution
public double getVariance()
Distribution
public double getStandardDeviation()
Distribution
public static double density(double alpha, double beta, double x)
public static double cdf(double alpha, double beta, double x)
public static double barF(double alpha, double beta, double x)
public static double inverseF(double alpha, double beta, double u)
public static LoglogisticDist getInstanceFromMLE(double[] x, int n)
x
- the list of observations to use to evaluate parametersn
- the number of observations to use to evaluate parameterspublic static double[] getMaximumLikelihoodEstimate(double[] x, int n)
x
- the list of observations to use to evaluate parametersn
- the number of observations to use to evaluate parameters
public static double getMean(double alpha, double beta)
public static double getVariance(double alpha, double beta)
public static double getStandardDeviation(double alpha, double beta)
public double getAlpha()
public double getBeta()
public void setParams(double alpha, double beta)
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