ConstantBernsteinFunction-class: Class for (almost) constant Bernstein functions

In hsloot/rmo: A package for the Marshall-Olkin distribution

Class for (almost) constant Bernstein functions

Description

A constant Bernstein function is a Bernstein function with only a constant part (for x > 0), i.e., b = 0 and \nu = 0. In particular,

\psi(x) = a , x > 0

Slots

constant

The nonnegative killing parameter (i.e., a in the representation)

See Also

calcIterativeDifference(), calcShockArrivalIntensities(), calcExShockArrivalIntensities(), calcExShockSizeArrivalIntensities(), calcMDCMGeneratorMatrix(), rextmo(), rpextmo()

Other Bernstein function classes: AlphaStableBernsteinFunction-class, BernsteinFunction-class, CompleteBernsteinFunction-class, CompositeScaledBernsteinFunction-class, ConvexCombinationOfBernsteinFunctions-class, ExponentialBernsteinFunction-class, GammaBernsteinFunction-class, InverseGaussianBernsteinFunction-class, LevyBernsteinFunction-class, LinearBernsteinFunction-class, ParetoBernsteinFunction-class, PoissonBernsteinFunction-class, ScaledBernsteinFunction-class, SumOfBernsteinFunctions-class

Other Bernstein function boundary classes: LinearBernsteinFunction-class, PoissonBernsteinFunction-class

Examples

# Create an object of class ConstantBernsteinFunction
ConstantBernsteinFunction()
ConstantBernsteinFunction(constant = 0.2)

# Evaluate the Bernstein function
bf <- ConstantBernsteinFunction(constant = 0.3)
calcIterativeDifference(bf, 1:5)

# Calculate shock-arrival intensities
bf <- ConstantBernsteinFunction(constant = 0.8)
calcShockArrivalIntensities(bf, 3)

# Calculate exchangeable shock-arrival intensities
bf <- ConstantBernsteinFunction(constant = 0.4)
calcExShockArrivalIntensities(bf, 3)

# Calculate exchangeable shock-size arrival intensities
bf <- ConstantBernsteinFunction(constant = 0.2)
calcExShockSizeArrivalIntensities(bf, 3)

# Calculate the Markov generator
bf <- ConstantBernsteinFunction(constant = 0.6)
calcMDCMGeneratorMatrix(bf, 3)

hsloot/rmo documentation built on May 1, 2024, 4:28 a.m.