Parameter Inference in a Hybrid System With Masked Data

We consider statistical inference for two fundamental hybrid systems based on masked data: series-parallel, and parallel-series. Under constant, and linear hazard functions for component life, we present the maximum likelihood, and interval estimation of parameters of interest. Simulation studies ar...

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Published in:IEEE transactions on reliability 2015-06, Vol.64 (2), p.636-644
Main Authors: Wang, Ronghua, Sha, Naijun, Gu, Beiqing, Xu, Xiaoling
Format: Article
Language:English
Subjects:
Density functional theory
Hazards
Interval estimation
masked data
Maximum likelihood estimation
parallel-series system
Probability
Reliability
series-parallel system
Technological innovation
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description We consider statistical inference for two fundamental hybrid systems based on masked data: series-parallel, and parallel-series. Under constant, and linear hazard functions for component life, we present the maximum likelihood, and interval estimation of parameters of interest. Simulation studies are performed to demonstrate the efficiency of the methods.
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subjects Density functional theory
Hazards
Interval estimation
masked data
Maximum likelihood estimation
parallel-series system
Probability
Reliability
series-parallel system
Technological innovation
title Parameter Inference in a Hybrid System With Masked Data
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