Statistical Inference in Dependent Component Hybrid Systems with Masked Data | Zendy

Naijun Sha | Zendy; Ronghua Wang | Zendy; PingAn Hu | Zendy; Xiaoling Xu | Zendy

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Statistical Inference in Dependent Component Hybrid Systems with Masked Data

Author(s) -

Naijun Sha,

Ronghua Wang,

PingAn Hu,

Xiaoling Xu

Publication year - 2015

Publication title -

advances in statistics

Language(s) - English

Resource type - Journals

eISSN - 2356-6892

pISSN - 2314-8314

DOI - 10.1155/2015/525136

Subject(s) - statistical inference , bivariate analysis , component (thermodynamics) , inference , series (stratigraphy) , computer science , simple (philosophy) , algorithm , hybrid system , exponential function , statistical model , fiducial inference , exponential family , mathematics , statistics , artificial intelligence , frequentist inference , machine learning , bayesian inference , bayesian probability , paleontology , mathematical analysis , philosophy , physics , epistemology , biology , thermodynamics

Complex systems are usually composed of simple hybrid systems. In this paper,we consider statistical inference for two fundamental hybrid systems: series-paralleland parallel-series systems based on masked data. Assuming dependent lifetimes ofcomponents modelled by Marshall and Olkin’s bivariate exponential distribution inthe system, we present maximum likelihood and interval estimation of parameters ofinterest. Intensive simulation studies are performed to demonstrate the efficiency ofthe methods

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