Density function, distribution function, quantile function, random generation,
raw moments, and limited moments for the Inverse Transformed Gamma
distribution with parameters shape1
, shape2
and
scale
.
dinvtrgamma(x, shape1, shape2, rate = 1, scale = 1/rate,
log = FALSE)
pinvtrgamma(q, shape1, shape2, rate = 1, scale = 1/rate,
lower.tail = TRUE, log.p = FALSE)
qinvtrgamma(p, shape1, shape2, rate = 1, scale = 1/rate,
lower.tail = TRUE, log.p = FALSE)
rinvtrgamma(n, shape1, shape2, rate = 1, scale = 1/rate)
minvtrgamma(order, shape1, shape2, rate = 1, scale = 1/rate)
levinvtrgamma(limit, shape1, shape2, rate = 1, scale = 1/rate,
order = 1)
dinvtrgamma
gives the density,
pinvtrgamma
gives the distribution function,
qinvtrgamma
gives the quantile function,
rinvtrgamma
generates random deviates,
minvtrgamma
gives the \(k\)th raw moment, and
levinvtrgamma
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 transformed gamma distribution with parameters
shape1
\(= \alpha\), shape2
\(= \tau\) and
scale
\(= \theta\), has density:
$$f(x) = \frac{\tau u^\alpha e^{-u}}{x \Gamma(\alpha)}, %
\quad u = (\theta/x)^\tau$$
for \(x > 0\), \(\alpha > 0\), \(\tau > 0\)
and \(\theta > 0\).
(Here \(\Gamma(\alpha)\) is the function implemented
by R's gamma()
and defined in its help.)
The inverse transformed gamma is the distribution of the random variable \(\theta X^{-1/\tau},\) where \(X\) has a gamma distribution with shape parameter \(\alpha\) and scale parameter \(1\) or, equivalently, of the random variable \(Y^{-1/\tau}\) with \(Y\) a gamma distribution with shape parameter \(\alpha\) and scale parameter \(\theta^{-\tau}\).
The inverse transformed gamma distribution defines a family of distributions with the following special cases:
An Inverse Gamma distribution when
shape2 == 1
;
An Inverse Weibull distribution when
shape1 == 1
;
An Inverse Exponential distribution when
shape1 == shape2 == 1
;
The \(k\)th raw moment of the random variable \(X\) is \(E[X^k]\), \(k < \alpha\tau\), and the \(k\)th limited moment at some limit \(d\) is \(E[\min(X, d)^k]\) for all \(k\).
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(dinvtrgamma(2, 3, 4, 5, log = TRUE))
p <- (1:10)/10
pinvtrgamma(qinvtrgamma(p, 2, 3, 4), 2, 3, 4)
minvtrgamma(2, 3, 4, 5)
levinvtrgamma(200, 3, 4, 5, order = 2)
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