Title
Identification for Systems With Binary Subsystems
Abstract
Consider a stochastic system of multiple subsystems, each subsystem having binary (“0” or “1”) output. The full system may have general binary or nonbinary (e.g., Gaussian) output. Such systems are widely encountered in practice, and include engineering systems for reliability, communications, and sensor networks, the collection of patients in a clinical trial, and Internet-based control systems. This paper considers the identification of parameters for such systems for general structural relationships between the subsystems and the full system. Maximum likelihood estimation (MLE) is used to estimate the mean output for the full system and the “success” probabilities for the subsystems. We present formal conditions for the convergence of the MLEs to the true full system and subsystem values as well as results on the asymptotic distributions for the MLEs. The MLE approach is well suited to providing asymptotic or finite-sample confidence bounds through the use of Fisher information or bootstrap Monte Carlo-based sampling.
Year
DOI
Venue
2014
10.1109/TAC.2013.2275664
Automatic Control, IEEE Transactions  
Keywords
Field
DocType
maximum likelihood estimation,stochastic systems,Fisher information,Internet-based control systems,MLE approach,asymptotic distributions,binary subsystems,bootstrap Monte Carlo-based sampling,clinical trial,engineering systems,finite-sample confidence bounds,formal conditions,maximum likelihood estimation,mean output estimation,multiple subsystems,parameter identification,patient collection,reliability,sensor networks,stochastic system,success probabilities,Complex systems,convergence analysis,maximum likelihood estimators,networks,reliability,system identification,uncertainty bounds
Convergence (routing),Monte Carlo method,Mathematical optimization,Gaussian,Fisher information,Control system,Maximum likelihood sequence estimation,Mathematics,Bootstrapping (electronics),Binary number
Journal
Volume
Issue
ISSN
59
1
0018-9286
Citations 
PageRank 
References 
9
0.92
18
Authors
1
Name
Order
Citations
PageRank
James C. Spall117823.16