accedaCRIShttps://accedacris.ulpgc.es/jspuiThe accedaCRIS digital repository system captures, stores, indexes, preserves, and distributes digital research material.Tue, 21 Mar 2023 11:51:04 GMT2023-03-21T11:51:04Z50151On zero-distorted generalized geometric distributionhttp://hdl.handle.net/10553/42444Title: On zero-distorted generalized geometric distribution
Authors: Sastry, D.V.S.; Bhati, Deepesh; Rattihalli, R. N.; Gómez-Déniz, E.
Abstract: We propose a new generalized geometric distribution which permits inflation/deflation of the zero count probability and study some of its properties. We also present an actuarial application of this distribution and fit it to three datasets used by other researchers. It is observed that the proposed distribution fits reasonably well to these data. Further, in a regression setup, the performance of this distribution is studied vis–a–vis other competing distributions used for explaining variability in a response variable.
Fri, 01 Jan 2016 00:00:00 GMThttp://hdl.handle.net/10553/424442016-01-01T00:00:00ZSpectral analysis with replicated time serieshttp://hdl.handle.net/10553/52420Title: Spectral analysis with replicated time series
Authors: Saavedra Santana, Pedro; Hernández, C. N.; Artiles, J.
Abstract: A doubly stochastic process {x(b,t);b∊B,t∊Z} is considered, with (B,β,Pβ) being a probability space so that for each b, {X(b,t);t ∊ Z} is a stationary process with an absolutely continuous spectral distribution. The population spectrum is defined as f(ω) = EB[Q(b,ω)] with Q(b,ω) being the spectral density function of X(b,t). The aim of this paper is to estimate f(ω) by means of a random sample b1,…,br from (B,β,Pβ). For each b1∊ B, the processes X(b1,t) are observed at the same times t=1,…,N. Thus, r time series (x(b1,t)} are available in order to estimate f(ω). A model for each individual periodogram, which involves f(ω), is formulated. It has been proven that a certain family of linear stationary processes follows the above model In this context, a kernel estimator is proposed in order to estimate f(ω). The bias, variance and asymptotic distribution of this estimator are investigated under certain conditions.
Sat, 01 Jan 2000 00:00:00 GMThttp://hdl.handle.net/10553/524202000-01-01T00:00:00ZA bivariate generalized geometric distribution with applicationshttp://hdl.handle.net/10553/41309Title: A bivariate generalized geometric distribution with applications
Authors: Gómez–Déniz, E.; Ghitany, M. E.; Ghitany, Ramesh C.
Abstract: This paper proposes a bivariate version of the univariate discrete generalized geometric distribution considered by Gomez-Deniz (2010). The proposed bivariate distribution can have a positive or negative correlation coefficient which can be used for modeling bivariate-dependent count data. After discussing some of its properties, maximum likelihood estimation is discussed. Two illustrative examples are given for fitting and demonstrating the usefulness of the new bivariate distribution proposed here.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10553/413092017-01-01T00:00:00ZA discrete distribution including the Poissonhttp://hdl.handle.net/10553/52568Title: A discrete distribution including the Poisson
Authors: Déniz, Emilio Gómez; Sarabia, José María
Abstract: This article presents a new generalization of the Poisson distribution, with the parameters α > 0 and θ > 0, using the Marshall and Olkin (1997) scheme and adding a parameter to the classical Poisson distribution. The particular case of α = 1 gives the Poisson distribution. The new distribution is unimodal and has a failure rate that monotonically increases or decreases depending on the value of the parameter α. After reviewing some of the properties of this distribution, we investigated the question of parameter estimation. Expected frequencies were calculated for two data sets, one with an index of dispersion larger than one and the other with an index of dispersion smaller than one. In both cases the distribution provided a very satisfactory fit.
Fri, 01 Jan 2016 00:00:00 GMThttp://hdl.handle.net/10553/525682016-01-01T00:00:00ZGamma-generalized inverse gaussian class of distributions with applicationshttp://hdl.handle.net/10553/42934Title: Gamma-generalized inverse gaussian class of distributions with applications
Authors: Gómez Déniz, Emilio; Calderín-Ojeda, Enrique; Sarabia, José María
Abstract: n 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.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10553/429342013-01-01T00:00:00ZThe Poisson-conjugate Lindley mixture distributionhttp://hdl.handle.net/10553/52532Title: The Poisson-conjugate Lindley mixture distribution
Authors: Gómez-Déniz, E.; Calderín-Ojeda, E.
Abstract: A new discrete distribution that depends on two parameters is introduced in this article. From this new distribution the geometric distribution is obtained as a special case. After analyzing some of its properties such as moments and unimodality, recurrences for the probability mass function and differential equations for its probability generating function are derived. In addition to this, parameters are estimated by maximum likelihood estimation numerically maximizing the log-likelihood function. Expected frequencies are calculated for different sets of data to prove the versatility of this discrete model.
Fri, 01 Jan 2016 00:00:00 GMThttp://hdl.handle.net/10553/525322016-01-01T00:00:00ZReference points for getting a target in a soccer competition. A probabilistic model with applications to the Spanish and English Premier Leaguehttp://hdl.handle.net/10553/54970Title: Reference points for getting a target in a soccer competition. A probabilistic model with applications to the Spanish and English Premier League
Authors: Gómez Déniz, Emilio; Dávila-Cárdenes, Nancy
Abstract: We model the probabilities that a soccer team gets a target, for example, to play the Champions League, the UEFA Europa League or preserve the category. Taking into account the points won until de mth matchday of the competition, when the winter transfer window is closed. We give closed-form expressions for the probabilities of reaching the goals. We also introduce a risk measure which is going to give us the smallest initial points needed to ensure that the probability of getting the target is larger than a given level.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10553/549702019-01-01T00:00:00ZA Marshall-Olkin family of heavy-tailed distributions which includes the lognormal onehttp://hdl.handle.net/10553/52579Title: A Marshall-Olkin family of heavy-tailed distributions which includes the lognormal one
Authors: García, V.J.; Gómez-Déniz, E.; Vázquez-Polo, F. J.
Abstract: A new class of heavy-tailed distribution functions, containing the lognormal distribution as a particular case is introduced. The class thus obtained depends on a set of three parameters, incorporating an additional distribution to the classical lognormal one. This new class of heavy-tailed distribution is presented as an alternative to other useful heavy-tailed distributions, such as the lognormal, Weibull, and Pareto distributions. The density and distribution functions of this new class are given by a closed expression which allows us to easily compute probabilities, quantiles, moments, and related measurements. Finally, some applications are shown as examples.
Fri, 01 Jan 2016 00:00:00 GMThttp://hdl.handle.net/10553/525792016-01-01T00:00:00ZComments on "Detecting Outliers in Gamma Distribution" by M. Jabbari Nooghabi etal. (2010)http://hdl.handle.net/10553/50298Title: Comments on "Detecting Outliers in Gamma Distribution" by M. Jabbari Nooghabi etal. (2010)
Authors: Lucini, M. Magdalena; Frery, Alejandro C.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10553/502982017-01-01T00:00:00ZParametric Lorenz curves based on the beta system of distributionshttp://hdl.handle.net/10553/106622Title: Parametric Lorenz curves based on the beta system of distributions
Authors: Gómez Déniz, Emilio; Sarabia, José María; Jordá, Vanesa
Abstract: Some families of Lorenz curves only approximate specific segments of the income distribution. This potential limitation motivates the consideration of more flexible models, which depart from a convenient form of the Lorenz curve. We begin with a Lorenz curve from which we generate a more flexible family of Lorenz curves through two new parameters. We explore the main properties of this family, including the Lorenz ordering, inequality measures, and Leimkhuler curves. An empirical application with US data suggests that the proposed models fit income data accurately.
Fri, 01 Jan 2021 00:00:00 GMThttp://hdl.handle.net/10553/1066222021-01-01T00:00:00ZA new count model generated from mixed Poisson transmuted exponential family with an application to health care datahttp://hdl.handle.net/10553/40344Title: A new count model generated from mixed Poisson transmuted exponential family with an application to health care data
Authors: Bhati, Deepesh; Kumawat, Pooja; Gómez–Déniz, E.
Abstract: In this article, a new mixed Poisson distribution is introduced. This
new distribution is obtained by utilizing mixing process, with Poisson
distribution as mixed distribution and Transmuted Exponential as mixing
distribution. Distributional properties like unimodality, moments,
overdispersion, infinite divisibility are studied. Three methods viz.
Method of moment, Method of moment and proportion, and
Maximum-likelihood method are used for parameter estimation. Further, an
actuarial application in context of aggregate claim distribution is
presented. Finally, to showthe applicability and superiority of proposed
model, we discuss count data and count regression modeling and compare
with somewell established models.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10553/403442017-01-01T00:00:00ZUsing a Bayesian hierarchical model for fitting automobile claim frequency datahttp://hdl.handle.net/10553/42951Title: Using a Bayesian hierarchical model for fitting automobile claim frequency data
Authors: Gómez-Déniz, E.; Sarabia, J. M.; Pérez-Sánchez, J. M.; Vazquez-Polo, F. J.
Abstract: In this article, we consider Bayesian statistical models in which prior distribution of the risk parameter is to be specified in a hierarchical fashion. The model obtained is shown to be over-dispersed and competitive with other models in the literature for fitting automobile claim frequency data. We obtain analytical forms of the distribution, which presents excellent properties. The methodology is illustrated by practical examples in setting automobile third party liability insurance, with which its sensitivity to the structure function is tested.
Tue, 01 Jan 2008 00:00:00 GMThttp://hdl.handle.net/10553/429512008-01-01T00:00:00ZMultivariate poisson-beta distributions with applicationshttp://hdl.handle.net/10553/42941Title: Multivariate poisson-beta distributions with applications
Authors: Sarabia, José María; Gómez Déniz, Emilio
Abstract: In this article we propose multivariate versions of the beta mixture of Poisson distribution considered by Gurland (1958), Katti (1966), and Holla and Bhattacharya (1965). The new class of distributions can be used for modeling multivariate dependent count data when marginal overdispersion is observed. After revising briefly some of their properties, a general multivariate model with Poisson-Beta marginals and with a flexible covariance structure is proposed. Several specific models as well as a model which admits correlations of any sign are considered. Estimation methods are discussed. Finally, examples of application for bivariate frequencies data are given.
Sat, 01 Jan 2011 00:00:00 GMThttp://hdl.handle.net/10553/429412011-01-01T00:00:00ZA simple method to study sensitivity of BMP'shttp://hdl.handle.net/10553/42607Title: A simple method to study sensitivity of BMP's
Authors: Gómez-Déniz, E.; Calderín-Ojeda, E.; Cabrera-Ortega, I.
Abstract: 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.
Sun, 01 Jan 2006 00:00:00 GMThttp://hdl.handle.net/10553/426072006-01-01T00:00:00ZMixture inverse Gaussian for unobserved heterogeneity in the autoregressive conditional duration modelhttp://hdl.handle.net/10553/40367Title: Mixture inverse Gaussian for unobserved heterogeneity in the autoregressive conditional duration model
Authors: Gómez–Déniz, Emilio; Pérez–Rodríguez, Jorge V.
Abstract: In this paper, we assume that the duration of a process has two different intrinsic components or phases which are independent. The first is the time it takes for a trade to be initiated in the market (for example, the time during which agents obtain knowledge about the market in which they are operating and accumulate information, which is coherent with Brownian motion) and the second is the subsequent time required for the trade to develop into a complete duration. Of course, if the first time is zero then the trade is initiated immediately and no initial knowledge is required. If we assume a specific compound Bernoulli distribution for the first time and an inverse Gaussian distribution for the second, the resulting convolution model has a mixture of an inverse Gaussian distribution with its reciprocal, which allows us to specify and test the unobserved heterogeneity in the autoregressive conditional duration (ACD) model.Our proposals make it possible not only to capture various density shapes of the durations but also easily to accommodate the behaviour of the tail of the distribution and the non monotonic hazard function. The proposed model is easy to fit and characterizes the behaviour of the conditional durations reasonably well in terms of statistical criteria based on point and density forecasts.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10553/403672017-01-01T00:00:00Z