uburrxii: The unit-Burr-XII distribution

In unitquantreg: Parametric Quantile Regression Models for Bounded Data

The unit-Burr-XII distribution

Description

Density function, distribution function, quantile function and random number generation function for the unit-Burr-XII distribution reparametrized in terms of the \tau-th quantile, \tau \in (0, 1).

Usage

duburrxii(x, mu, theta, tau = 0.5, log = FALSE)

puburrxii(q, mu, theta, tau = 0.5, lower.tail = TRUE, log.p = FALSE)

quburrxii(p, mu, theta, tau = 0.5, lower.tail = TRUE, log.p = FALSE)

ruburrxii(n, mu, theta, tau = 0.5)

Arguments

x, q	vector of positive quantiles.
mu	location parameter indicating the \tau-th quantile, \tau \in (0, 1).
theta	nonnegative shape parameter.
tau	the parameter to specify which quantile is to used.
log, log.p	logical; If TRUE, probabilities p are given as log(p).
lower.tail	logical; If TRUE, (default), P(X \leq{x}) are returned, otherwise P(X > x).
p	vector of probabilities.
n	number of observations. If length(n) > 1, the length is taken to be the number required.

Details

Probability density function

f(y\mid \alpha, \theta )=\frac{\alpha \theta }{y}\left[ -\log (y)\right]^{\theta -1}\left\{ 1+\left[ -\log (y)\right] ^{\theta }\right\} ^{-\alpha -1}

Cumulative distribution function

F(y\mid \alpha, \theta )=\left\{ 1+\left[ -\log (y)\right] ^{\theta}\right\} ^{-\alpha }

Quantile function

Q(\tau \mid \alpha, \theta )=\exp \left[ -\left( \tau ^{-\frac{1}{\alpha }}-1\right)^{\frac{1}{\theta }} \right]

Reparameterization

\alpha=g^{-1}(\mu)=\frac{\log\left ( \tau^{-1} \right )}{\log\left [ 1+\log\left ( \frac{1}{\mu} \right )^\theta \right ]}

Value

duburrxii gives the density, puburrxii gives the distribution function, quburrxii gives the quantile function and ruburrxii generates random deviates.

Invalid arguments will return an error message.

Author(s)

Josmar Mazucheli [e-mail]

André F. B. Menezes [e-mail]

References

Korkmaz M. C. and Chesneau, C., (2021). On the unit Burr-XII distribution with the quantile regression modeling and applications. Computational and Applied Mathematics, 40(29), 1–26.

Examples

set.seed(123)
x <- ruburrxii(n = 1000, mu = 0.5, theta = 1.5, tau = 0.5)
R <- range(x)
S <- seq(from = R[1], to = R[2], by =  0.01)
hist(x, prob = TRUE, main = 'unit-Burr-XII')
lines(S, duburrxii(x = S, mu = 0.5, theta = 1.5, tau = 0.5), col = 2)
plot(ecdf(x))
lines(S, puburrxii(q = S, mu = 0.5, theta = 1.5, tau = 0.5), col = 2)
plot(quantile(x, probs = S), type = "l")
lines(quburrxii(p = S, mu = 0.5, theta = 1.5, tau = 0.5), col = 2)

unitquantreg documentation built on Sept. 8, 2023, 5:40 p.m.