Geodesic
Compound geometric and Poisson models
Kybernetika, Tome 51 (2015) no. 6, pp. 933-959
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Many lifetime distributions are motivated only by mathematical interest. Here, eight new families of distributions are introduced. These distributions are motivated as models for the stress of a system consisting of components working in parallel/series and each component has a fixed number of sub-components working in parallel/series. Mathematical properties and estimation procedures are derived for one of the families of distributions. A real data application shows superior performance of a three-parameter distribution (performance assessed with respect to Kolmogorov-Smirnov statistics, AIC values, BIC values, CAIC values, AICc values, HQC values, probability-probability plots, quantile-quantile plots and density plots) versus thirty one other distributions, each having at least three parameters.
Many lifetime distributions are motivated only by mathematical interest. Here, eight new families of distributions are introduced. These distributions are motivated as models for the stress of a system consisting of components working in parallel/series and each component has a fixed number of sub-components working in parallel/series. Mathematical properties and estimation procedures are derived for one of the families of distributions. A real data application shows superior performance of a three-parameter distribution (performance assessed with respect to Kolmogorov-Smirnov statistics, AIC values, BIC values, CAIC values, AICc values, HQC values, probability-probability plots, quantile-quantile plots and density plots) versus thirty one other distributions, each having at least three parameters.
DOI : 10.14736/kyb-2015-6-0933
Classification : 62E99
Keywords: exponential distribution; exponentiated exponential distribution; maximum likelihood estimation 
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Hakamipour, Nooshin; Rezaei, Sadegh; Nadarajah, Saralees. Compound geometric and Poisson models. Kybernetika, Tome 51 (2015) no. 6, pp. 933-959. doi: 10.14736/kyb-2015-6-0933

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