A unified approach for representing wireless channels using EM-based finite mixture of gamma distributions

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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Finite mixture of gamma distributions: A conjugate prior

Computational Statistics & Data Analysis ◽

10.1016/j.csda.2006.06.005 ◽

2007 ◽

Vol 51 (9) ◽

pp. 4369-4378 ◽

Cited By ~ 11

Author(s):

Jamal A. Al-Saleh ◽

Satish K. Agarwal

Keyword(s):

Finite Mixture ◽

Conjugate Prior ◽

Gamma Distributions

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Estimation of parameters in a finite mixture of multivariate gamma distributions using gaussian approximation

Sri Lankan Journal of Applied Statistics ◽

10.4038/sljastats.v17i3.7902 ◽

2016 ◽

Vol 17 (3) ◽

pp. 187 ◽

Cited By ~ 2

Author(s):

V. S. Vaidyanathan ◽

R. Vani Lakshmi

Keyword(s):

Gaussian Approximation ◽

Finite Mixture ◽

Estimation Of Parameters ◽

Gamma Distributions

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Finite mixture-of-gamma distributions: estimation, inference, and model-based clustering

Advances in Data Analysis and Classification ◽

10.1007/s11634-019-00361-y ◽

2019 ◽

Vol 13 (4) ◽

pp. 1053-1082

Author(s):

Derek S. Young ◽

Xi Chen ◽

Dilrukshi C. Hewage ◽

Ricardo Nilo-Poyanco

Keyword(s):

Finite Mixture ◽

Model Based Clustering ◽

Model Based ◽

Gamma Distributions

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A Bivariate Gamma Distribution Whose Marginals are Finite Mixtures of Gamma Distributions

Statistics Optimization & Information Computing ◽

10.19139/soic-2310-5070-1001 ◽

2020 ◽

Vol 8 (4) ◽

pp. 950-971

Author(s):

Maryam Rafiei ◽

Anis Iranmanesh ◽

Daya k. Nagar

Keyword(s):

Maximum Likelihood ◽

Gamma Distribution ◽

Maximum Likelihood Method ◽

Correlation Coefficients ◽

Moment Generating Function ◽

Bivariate Distribution ◽

Finite Mixture ◽

Likelihood Method ◽

Gamma Distributions ◽

Bivariate Gamma Distribution

In this article a new bivariate distribution, whose both the marginals are finite mixture of gamma distribution has been defined. Several of its properties such moments, correlation coefficients, measure of skewness, moment generating function, Renyi and Shannon entropies have been derived. Simulation study have been conducted to evaluate the performance of maximum likelihood method.

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