Enrique Calderín-Ojeda

The discrete Lindley distribution: properties and applications

Modelling count data is one of the most important issues in statistical research. In this paper, a new probability mass function is introduced by discretizing the continuous failure model of the Lindley distribution. The model obtained is over-dispersed and competitive with the Poisson distribution to fit automobile claim frequency data. After revising some of its properties a compound discrete Lindley distribution is obtained in closed form. This model is suitable to be applied in the collective risk model when both number of claims and size of a single claim are implemented into the model. The new compound distribution fades away to zero much more slowly than the classical compound Poisson distribution, being therefore suitable for modelling extreme data.

Modeling claims data with composite Stoppa models

In this paper, a new class of composite model is proposed for modeling actuarial claims data of mixed sizes. The model is developed using the Stoppa distribution and a mode-matching procedure. The use of the Stoppa distribution allows for more flexibility over the thickness of the tail, and the mode-matching procedure gives a simple derivation of the model compositing with a variety of distributions. In particular, the Weibull–Stoppa and the Lognormal–Stoppa distributions are investigated. Their performance is compared with existing composite models in the context of the well-known Danish fire insurance data-set. The results suggest the composite Weibull–Stoppa model outperforms the existing composite models in all seven goodness-of-fit measures considered.

Parameters Estimation for a New Generalized Geometric Distribution

A generalization of the geometric distribution is obtained after mixing the Poisson distribution with the generalized exponential distribution in Marshall and Olkin in 1997. The discrete distribution is defined through a new special function also introduced in this manuscript. Unimodality is highlighted among the properties of this two-parameter distribution. A favorable maximum likelihood parameter estimation scheme for the discrete distribution is introduced based on a quadrature method by approximating the special function by the trapezoidal rule. The effectiveness of the method is confirmed by expectation–maximization (EM) algorithm. An application to explain the demand for health services is given.

Gamma-Generalized Inverse Gaussian Class of Distributions with Applications

In this article, a new family of probability distributions with domain in ℝ+ is introduced. This class can be considered as a natural extension of the exponential-inverse Gaussian distribution in Bhattacharya and Kumar (1986) and Frangos and Karlis (2004). This new family is obtained through the mixture of gamma distribution with generalized inverse Gaussian distribution. We also show some important features such as expressions of probability density function, moments, etc. Special attention is paid to the mixture with the inverse Gaussian distribution, as a particular case of the generalized inverse Gaussian distribution. From the exponential-inverse Gaussian distribution two one-parameter family of distributions are obtained to derive risk measures and credibility expressions. The versatility of this family has been proven in numerical examples.

Unconditional distributions obtained from conditional specification models with applications in risk theory

Bivariate distributions, specified in terms of their conditional distributions, provide a powerful tool to obtain flexible distributions. These distributions play an important role in specifying the conjugate prior in certain multi-parameter Bayesian settings. In this paper, the conditional specification technique is applied to look for more flexible distributions than the traditional ones used in the actuarial literature, as the Poisson, negative binomial and others. The new specification draws inferences about parameters of interest in problems appearing in actuarial statistics. Two unconditional (discrete) distributions obtained are studied and used in the collective risk model to compute the right-tail probability of the aggregate claim size distribution. Comparisons with the compound Poisson and compound negative binomial are made.

On the Composite Weibull–Burr Model to describe claim data

ABSTRACT In this article, a new continuous composite model, useful for modeling losses that combine a mixture of moderate and large claims, is presented. The probability density function of this family, the composite Weibull–Burr distribution, is unimodal and positively skewed with a thick upper tail. This model is derived, via a mode-matching procedure, by using the Weibull distribution up to the modal value (threshold) and the scaled Burr density thereafter. This methodology ensures not only the continuity and differentiability conditions at the threshold, but it also facilitates its computational implementation. The performance of this composite model is compared with the Weibull–Burr spliced distribution, continuous composite Weibull–Pareto and Weibull–Pareto spliced families, and the GB2 distribution in the context of the well-known Danish fire insurance dataset.

A Simple Method to Study Sensitivity of BMP's

In this article we measure the local or infinitesimal sensitivity of a kind of Bayes estimates which appear in bonus–malus systems. Bonus–malus premiums can be viewed as a functional depending on the prior distribution. To measure when small changes in the prior cause large changes in the premium we compute the norm of the Fréchet derivative and propose a simple procedure to decide if a bonus–malus premium is robust. As an application, an example where the risk has a Poisson distribution and its parameter follows a Gamma prior distribution is presented under the net and variance premium principles.

The multivariate negative binomial–Lindley distribution. Properties and new representation for the univariate case

Abstract In this paper a multivariate negative binomial–Lindley distribution is introduced. The univariate version of this distribution comprises an alternative of the model proposed by Zamani and Ismail (2010). With respect to this latter model, this representation presents some advantages since it provides a tractable model with attractive properties that makes it suitable for application in any field where overdispersion is observed. Some properties of the model are studied including a recurrence for the probabilities of the multivariate and univariate distribution. An EM algorithm is derived to estimate the parameters of this multivariate distribution. Finally, some numerical applications for the univariate and bivariate cases are given to illustrate the performance of this model.

Properties and applications of the Poisson-reciprocal inverse Gaussian distribution

ABSTRACT The barely known continuous reciprocal inverse Gaussian distribution is used in this paper to introduce the Poisson-reciprocal inverse Gaussian discrete distribution. Several of its most relevant statistical properties are examined, some of them directly inherited from the reciprocal of the inverse Gaussian distribution. Furthermore, a mixed Poisson regression model that uses the reciprocal inverse Gaussian as mixing distribution is presented. Parameters estimation in this regression model is performed via an EM type algorithm. In light of the numerical results displayed in the paper, the distributions introduced in this work are competitive with the classical negative binomial and Poisson-inverse Gaussian distributions.

The Geometric ArcTan distribution with applications to model demand for health services

ABSTRACT In this paper , a new discrete two–parameter distribution α ∈ ℜ − {0} and 0 < θ < 1, the Geometric ArcTan (GAT) distribution is introduced. The geometric distribution is a limiting case of this model when α tends to zero. Similarly to the the latter distribution, this probabilistic family is unimodal but the mode can be located at zero or in other point of the support. Then, after deriving some of its more relevant properties , the issue of parameter investigation is investigated. Next, the GAT distribution is used to explain the demand for health services by means of a regression model. Numerical results show that this new model outperforms the negative binomial distribution.