545 research outputs found
Random variate generation and connected computational issues for the Poisson–Tweedie distribution
After providing a systematic outline of the stochastic genesis of the Poisson–Tweedie distribution, some computational issues are considered. More specifically, we introduce a closed form for the probability function, as well as its corresponding integral representation which may be useful for large argument values. Several algorithms for generating Poisson–Tweedie random variates are also suggested. Finally, count data connected to the citation profiles of two statistical journals are modeled and analyzed by means of the Poisson–Tweedie distribution
Generalised linear models for aggregate claims; to Tweedie or not?
The compound Poisson distribution with gamma claim
sizes is a very common model for premium
estimation in Property and Casualty
insurance. Under this distributional assumption,
generalised linear models (GLMs) are used to
estimate the mean claim frequency and severity,
then these estimators are simply multiplied to
estimate the mean aggregate loss.
The Tweedie distribution allows to parametrise the
compound Poisson-gamma (CPG) distribution as a
member of the exponential dispersion family and
then fit a GLM with a CPG distribution for the
response. Thus, with the Tweedie distribution it
is possible to estimate the mean aggregate loss
using GLMs directly, without the need to
previously estimate the mean frequency and
severity separately.
The purpose of this educational note is to explore
the differences between these two estimation
methods, contrasting the advantages and
disadvantages of each
Flexible Tweedie regression models for continuous data
Tweedie regression models provide a flexible family of distributions to deal
with non-negative highly right-skewed data as well as symmetric and heavy
tailed data and can handle continuous data with probability mass at zero. The
estimation and inference of Tweedie regression models based on the maximum
likelihood method are challenged by the presence of an infinity sum in the
probability function and non-trivial restrictions on the power parameter space.
In this paper, we propose two approaches for fitting Tweedie regression models,
namely, quasi- and pseudo-likelihood. We discuss the asymptotic properties of
the two approaches and perform simulation studies to compare our methods with
the maximum likelihood method. In particular, we show that the quasi-likelihood
method provides asymptotically efficient estimation for regression parameters.
The computational implementation of the alternative methods is faster and
easier than the orthodox maximum likelihood, relying on a simple Newton scoring
algorithm. Simulation studies showed that the quasi- and pseudo-likelihood
approaches present estimates, standard errors and coverage rates similar to the
maximum likelihood method. Furthermore, the second-moment assumptions required
by the quasi- and pseudo-likelihood methods enables us to extend the Tweedie
regression models to the class of quasi-Tweedie regression models in the
Wedderburn's style. Moreover, it allows to eliminate the non-trivial
restriction on the power parameter space, and thus provides a flexible
regression model to deal with continuous data. We provide \texttt{R}
implementation and illustrate the application of Tweedie regression models
using three data sets.Comment: 34 pages, 8 figure
A generalized Fellner-Schall method for smoothing parameter estimation with application to Tweedie location, scale and shape models
We consider the estimation of smoothing parameters and variance components in
models with a regular log likelihood subject to quadratic penalization of the
model coefficients, via a generalization of the method of Fellner (1986) and
Schall (1991). In particular: (i) we generalize the original method to the case
of penalties that are linear in several smoothing parameters, thereby covering
the important cases of tensor product and adaptive smoothers; (ii) we show why
the method's steps increase the restricted marginal likelihood of the model,
that it tends to converge faster than the EM algorithm, or obvious
accelerations of this, and investigate its relation to Newton optimization;
(iii) we generalize the method to any Fisher regular likelihood. The method
represents a considerable simplification over existing methods of estimating
smoothing parameters in the context of regular likelihoods, without sacrificing
generality: for example, it is only necessary to compute with the same first
and second derivatives of the log-likelihood required for coefficient
estimation, and not with the third or fourth order derivatives required by
alternative approaches. Examples are provided which would have been impossible
or impractical with pre-existing Fellner-Schall methods, along with an example
of a Tweedie location, scale and shape model which would be a challenge for
alternative methods
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