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Activity Number: 38 - Reliability Insights
Type: Contributed
Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
Sponsor: Quality and Productivity Section
Abstract #323574
Title: Convergence Results for Identification of Systems with Binary Subsystems
Author(s): Long Wang* and James C Spall
Companies: Johns Hopkins University and Applied Physics Laboratory
Keywords: Convergence analysis ; maximum likelihood estimators ; system identification
Abstract:

Consider a stochastic system composed of multiple subsystems, where each subsystem can generate a binary response. The full system follows a general canonical exponential family distribution (e.g., Gaussian and multinomial) that depends on multiple parameters. This type of system has a wide range of applications in practice, such as systems reliability testing, sensor networks, target detection, fault diagnosis, and Internet-based systems control. Based on principles of maximum likelihood estimation, prior work introduces a method to estimate the mean output (reliability) of the full system and the "success" probabilities of the subsystems. This paper generalized the results to estimate all the unknown parameters in the exponential family distribution (e.g., means and variances of a Gaussian distribution). We derive an MLE formulation for general structural relationships between the subsystems and the full system along with the formal convergence proof of the MLEs. The asymptotic distributions of the MLEs are also given, which is then used to provide asymptotic confidence bounds through the Fisher information matrix.


Authors who are presenting talks have a * after their name.

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