mlpower | R Documentation |
The maximum likelihood estimate of alpha
is the maximum of x
+
epsilon
(see the details) and the maximum likelihood estimate of
beta
is 1/(log(alpha)-mean(log(x)))
.
mlpower(x, na.rm = FALSE, ...)
x |
a (non-empty) numeric vector of data values. |
na.rm |
logical. Should missing values be removed? |
... |
|
For the density function of the power distribution see
PowerDist. The maximum likelihood estimator of
alpha
does not exist, strictly
speaking. This is because x
is supported c(0, alpha)
with
an open endpoint on alpha in the extraDistr
implementation of
dpower
. If the endpoint was closed, max(x)
would have been
the maximum likelihood estimator. To overcome this problem, we add
a possibly user specified epsilon
to max(x)
.
mlpower
returns an object of class univariateML
.
This is a named numeric vector with maximum likelihood estimates for
alpha
and beta
and the following attributes:
model |
The name of the model. |
density |
The density associated with the estimates. |
logLik |
The loglikelihood at the maximum. |
support |
The support of the density. |
n |
The number of observations. |
call |
The call as captured my |
Arslan, G. "A new characterization of the power distribution." Journal of Computational and Applied Mathematics 260 (2014): 99-102.
PowerDist for the power density. Pareto for the closely related Pareto distribution.
mlpower(precip)
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