The Log–Lindley distribution as an alternative to the beta regression model with applications in insurance

Gómez–Déniz, Emilio; Sordo, Miguel Á.; Calderín–Ojeda, Enrique

doi:10.1016/j.insmatheco.2013.10.017

Cited by 106 publications

(56 citation statements)

References 23 publications

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“…Furthermore, the analysis of data in point 2 has connections with the contributions of Loannides, Overman (2003), Nitsch (2005), Newman (2005), Giesen et al (2010), Gómez-Déniz et al (2014), Gómez-Déniz, Calderín-Ojeda (2015), Shujuan (2016), Luckstead, Devadoss (2017), that tried to find the best distributions to fit better the frequency of cities by size. Actually, although deserving further attention in future works, point 2 shows that Pareto distributions seem to fit most countries of the sample, Log normal distributions are better for countries with remote and small cities and a combination of distributions are the outcome of situations when first cities empty second ones.…”

Section: Discussionmentioning

confidence: 99%

“…Cordoba (2008) shows that the profile of the Zipf curve comes from the regularity of the growth process across cities. Many authors test the validity of different distributions to explain the population size of the cities (Loannides, Overman 2003, Nitsch 2005, Newman 2005, Giesen et al 2010, Gómez-Déniz et al 2014, Gómez-Déniz, Calderín-Ojeda 2015, Shujuan 2016, Luckstead, Devadoss 2017.…”

Section: Introductionmentioning

confidence: 99%

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Urban Concentration and Spatial Allocation of Rents from natural resources. A Zipf's Curve Approach

Dentinho

2017

REGION

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Abstract. This paper aims at demonstrating how countries' dependency on natural resources plays a crucial role in urban concentration. The Zipf curve elasticity is estimated for a group of countries and related to a set of indicators of unilateral transferences. Results show that in comparison to others, countries with higher urban concentration explained by higher Zipf curve elasticity have a higher percentage of income coming from natural resources and education expenditures whereas public spending in health and net outflow of foreign direct investment seem to have spatial redistribution effects. Summing up, there are signs that the spatial allocation of property rights over natural resources and related rents influences urban concentration.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Urban Concentration and Spatial Allocation of Rents from natural resources. A Zipf's Curve Approach

Dentinho

2017

REGION

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“…For instance, Reference [6] used the Lindley distribution to study the reliability of application strength systems. Considering the generalized Lindley distribution, Reference [7] came up with a new bounded domain probability density function, and introduced a distorted premium principle based on the special category of this distribution.…”

Section: Lindley Distributionmentioning

confidence: 99%

Parameter Estimation of Lindley Distribution Based on Progressive Type-II Censored Competing Risks Data with Binomial Removals

Nie

Gui

2019

Mathematics

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The competing risk model based on Lindley distribution is discussed under the progressive type-II censored sample data with binomial removals. The maximum likelihood estimation of the unknown parameters of the distribution is established. Using the Lindley approximation method, we also obtain the Bayesian estimation of the unknown parameters of the distribution under different loss functions. The performance of different estimates is studied in this article. A real practical dataset is analyzed for illustration.

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“…Many different regression models have been proposed in the past two decades for modeling this type of data. For example, Buckley (2003), Ferrari & Cribari-Neto (2004), Paz et al (2015), Lemonte & Bazán (2016), Gómez-Déniz et al (2014) and Bayes et al (2017) have all proposed regression models.…”

Section: Introductionmentioning

confidence: 99%

L-Logistic regression models: Prior sensitivity analysis, robustness to outliers and applications

Paz¹,

Bazán²

2019

Braz. J. Probab. Stat.

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Tadikamalla & Johnson (1982) developed the L B distribution to variables with bounded support by considering a transformation of the standard Logistic distribution. In this manuscript, a convenient parametrization of this distribution is proposed in order to develop regression models. This distribution, referred to here as L-Logistic distribution, provides great flexibility and includes the uniform distribution as a particular case. Several properties of this distribution are studied, and a Bayesian approach is adopted for the parameter estimation. Simulation studies, considering prior sensitivity analysis, recovery of parameters and comparison of algorithms, and robustness to outliers are all discussed showing that the results are insensitive to the choice of priors, efficiency of the algorithm MCMC adopted, and robustness of the model when compared with the beta distribution. Applications to estimate the vulnerability to poverty and to explain the anxiety are performed. The results to applications show that the L-Logistic regression models provide a better fit than the corresponding beta regression models.

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The Log–Lindley distribution as an alternative to the beta regression model with applications in insurance

Cited by 106 publications

References 23 publications

Urban Concentration and Spatial Allocation of Rents from natural resources. A Zipf's Curve Approach

Urban Concentration and Spatial Allocation of Rents from natural resources. A Zipf's Curve Approach

Parameter Estimation of Lindley Distribution Based on Progressive Type-II Censored Competing Risks Data with Binomial Removals

L-Logistic regression models: Prior sensitivity analysis, robustness to outliers and applications

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