Density function, distribution function, quantile function, random
generation, raw moments and limited moments for the Inverse Burr
distribution with parameters shape1
, shape2
and
scale
.
dinvburr(x, shape1, shape2, rate = 1, scale = 1/rate,
log = FALSE)
pinvburr(q, shape1, shape2, rate = 1, scale = 1/rate,
lower.tail = TRUE, log.p = FALSE)
qinvburr(p, shape1, shape2, rate = 1, scale = 1/rate,
lower.tail = TRUE, log.p = FALSE)
rinvburr(n, shape1, shape2, rate = 1, scale = 1/rate)
minvburr(order, shape1, shape2, rate = 1, scale = 1/rate)
levinvburr(limit, shape1, shape2, rate = 1, scale = 1/rate,
order = 1)
dinvburr
gives the density,
invburr
gives the distribution function,
qinvburr
gives the quantile function,
rinvburr
generates random deviates,
minvburr
gives the \(k\)th raw moment, and
levinvburr
gives the \(k\)th moment of the limited loss
variable.
Invalid arguments will result in return value NaN
, with a warning.
vector of quantiles.
vector of probabilities.
number of observations. If length(n) > 1
, the length is
taken to be the number required.
parameters. Must be strictly positive.
an alternative way to specify the scale.
logical; if TRUE
, probabilities/densities
\(p\) are returned as \(\log(p)\).
logical; if TRUE
(default), probabilities are
\(P[X \le x]\), otherwise, \(P[X > x]\).
order of the moment.
limit of the loss variable.
Vincent Goulet vincent.goulet@act.ulaval.ca and Mathieu Pigeon
The inverse Burr distribution with parameters shape1
\(=
\tau\), shape2
\(= \gamma\) and scale
\(= \theta\), has density:
$$f(x) = \frac{\tau \gamma (x/\theta)^{\gamma \tau}}{%
x [1 + (x/\theta)^\gamma]^{\tau + 1}}$$
for \(x > 0\), \(\tau > 0\), \(\gamma > 0\) and
\(\theta > 0\).
The inverse Burr is the distribution of the random variable $$\theta \left(\frac{X}{1 - X}\right)^{1/\gamma},$$ where \(X\) has a beta distribution with parameters \(\tau\) and \(1\).
The inverse Burr distribution has the following special cases:
A Loglogistic distribution when shape1
== 1
;
An Inverse Pareto distribution when
shape2 == 1
;
An Inverse Paralogistic distribution
when shape1 == shape2
.
The \(k\)th raw moment of the random variable \(X\) is \(E[X^k]\), \(-\tau\gamma < k < \gamma\).
The \(k\)th limited moment at some limit \(d\) is \(E[\min(X, d)^k]\), \(k > -\tau\gamma\) and \(1 - k/\gamma\) not a negative integer.
Kleiber, C. and Kotz, S. (2003), Statistical Size Distributions in Economics and Actuarial Sciences, Wiley.
Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2012), Loss Models, From Data to Decisions, Fourth Edition, Wiley.
exp(dinvburr(2, 2, 3, 1, log = TRUE))
p <- (1:10)/10
pinvburr(qinvburr(p, 2, 3, 1), 2, 3, 1)
## variance
minvburr(2, 2, 3, 1) - minvburr(1, 2, 3, 1) ^ 2
## case with 1 - order/shape2 > 0
levinvburr(10, 2, 3, 1, order = 2)
## case with 1 - order/shape2 < 0
levinvburr(10, 2, 1.5, 1, order = 2)
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