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Statistical Inference in Dependent Component Hybrid Systems with Masked Data


Affiliations
1 Department of Mathematical Sciences, University of Texas at El Paso, El Paso, TX 79968, United States
2 College of Mathematics and Science, Shanghai Normal University, Shanghai 200234, China
3 Business Information Management School, Shanghai University of International Business and Economics, Shanghai 201600, China
 

Complex systems are usually composed of simple hybrid systems. In this paper,we consider statistical inference for two fundamental hybrid systems: series-parallel and parallel-series systems based on masked data. Assuming dependent lifetimes of components modelled by Marshall and Olkin's bivariate exponential distribution in the system, we present maximum likelihood and interval estimation of parameters of interest. Intensive simulation studies are performed to demonstrate the efficiency of the methods.
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  • Statistical Inference in Dependent Component Hybrid Systems with Masked Data

Abstract Views: 115  |  PDF Views: 14

Authors

Naijun Sha
Department of Mathematical Sciences, University of Texas at El Paso, El Paso, TX 79968, United States
Ronghua Wang
College of Mathematics and Science, Shanghai Normal University, Shanghai 200234, China
Ping Hu
College of Mathematics and Science, Shanghai Normal University, Shanghai 200234, China
Xiaoling Xu
Business Information Management School, Shanghai University of International Business and Economics, Shanghai 201600, China

Abstract


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