Analysis of UWB communication over IEEE 802.15.3a channel by superseding lognormal shadowing by Mixture of Gamma distributions

AbstractThe dynamics of the wild boar population has become a pressing issue not only for ecological purposes, but also for agricultural and livestock production. The data related to the wild boar dispersal distance can have a complex structure, including excess of zeros and right-censored observations, thus being challenging for modeling. In this sense, we propose two different zero-inflated-right-censored regression models, assuming Weibull and gamma distributions. First, we present the construction of the likelihood function, and then, we apply both models to simulated datasets, demonstrating that both regression models behave well. The simulation results point to the consistency and asymptotic unbiasedness of the developed methods. Afterwards, we adjusted both models to a simulated dataset of wild boar dispersal, including excess of zeros, right-censored observations, and two covariates: age and sex. We showed that the models were useful to extract inferences about the wild boar dispersal, correctly describing the data mimicking a situation where males disperse more than females, and age has a positive effect on the dispersal of the wild boars. These results are useful to overcome some limitations regarding inferences in zero-inflated-right-censored datasets, especially concerning the wild boar’s population. Users will be provided with an R function to run the proposed models.

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Survival and Reliability Analysis with an Epsilon-Positive Family of Distributions with Applications

Symmetry ◽

10.3390/sym13050908 ◽

2021 ◽

Vol 13 (5) ◽

pp. 908

Author(s):

Perla Celis ◽

Rolando de la Cruz ◽

Claudio Fuentes ◽

Héctor W. Gómez

Keyword(s):

Survival Data ◽

Maximum Likelihood Estimates ◽

New Class ◽

New Family ◽

Gamma Distributions ◽

Special Cases ◽

Log Normal ◽

Positive Support ◽

Positive Distributions ◽

Family Of Distributions

We introduce a new class of distributions called the epsilon–positive family, which can be viewed as generalization of the distributions with positive support. The construction of the epsilon–positive family is motivated by the ideas behind the generation of skew distributions using symmetric kernels. This new class of distributions has as special cases the exponential, Weibull, log–normal, log–logistic and gamma distributions, and it provides an alternative for analyzing reliability and survival data. An interesting feature of the epsilon–positive family is that it can viewed as a finite scale mixture of positive distributions, facilitating the derivation and implementation of EM–type algorithms to obtain maximum likelihood estimates (MLE) with (un)censored data. We illustrate the flexibility of this family to analyze censored and uncensored data using two real examples. One of them was previously discussed in the literature; the second one consists of a new application to model recidivism data of a group of inmates released from the Chilean prisons during 2007. The results show that this new family of distributions has a better performance fitting the data than some common alternatives such as the exponential distribution.

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mvClaim : an R package for multivariate general insurance claims severity modelling

Annals of Actuarial Science ◽

10.1017/s1748499521000099 ◽

2021 ◽

pp. 1-17

Author(s):

Sen Hu ◽

T. Brendan Murphy ◽

Adrian O’Hagan

Keyword(s):

R Package ◽

Finite Mixture ◽

Current Version ◽

Mixture Of Experts ◽

Insurance Claim ◽

General Insurance ◽

Gamma Distributions ◽

Approach Theory ◽

Modelling Approach ◽

Model Family

Abstract The mvClaim package in R provides flexible modelling frameworks for multivariate insurance claim severity modelling. The current version of the package implements a parsimonious mixture of experts (MoE) model family with bivariate gamma distributions, as introduced in Hu et al., and a finite mixture of copula regressions within the MoE framework as in Hu & O’Hagan. This paper presents the modelling approach theory briefly and the usage of the models in the package in detail. This package is hosted on GitHub at https://github.com/senhu/.

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