Economics - Research Publications

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End date: 2019 × Type: Journal Article × Author: Calderin, Enrique × Start date: 2016 ×

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    The Poisson-conjugate Lindley mixture distribution
    Gómez-Déniz, E ; Calderín-Ojeda, E (Taylor & Francis, 2016-05-18)
    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.
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    The distribution of all French communes: A composite parametric approach
    Calderin-Ojeda, E (Elsevier, 2016-05-15)
    The distribution of the size of all French settlements (communes) from 1962 to 2012 is examined by means of a three-parameter composite Lognormal–Pareto distribution. This model is based on a Lognormal density up to an unknown threshold value and a Pareto density thereafter. Recent findings have shown that the untruncated settlement size data is in excellent agreement with the Lognormal distribution in the lower and central parts of the empirical distribution, but it follows a power law in the upper tail. For that reason, this probabilistic family, that nests both models, seems appropriate to describe urban agglomeration in France. The outcomes of this paper reveal that for the early periods (1962–1975) the upper quartile of the commune size data adheres closely to a power law distribution, whereas for later periods (2006–2012) most of the city size dynamics is explained by a Lognormal model.
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    Modeling claims data with composite Stoppa models
    Calderín-Ojeda, E ; Kwok, CF (Taylor & Francis, 2016)
    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.