Gradient analysis of Markov-type control schemes and its applications

DOI10.1080/03610918.2019.1687718OpenAlexW2986021621MaRDI QIDQ5082933

Emmanuel Yashchin

Publication date: 21 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2019.1687718

zbMATH Keywords

detectionmonitoringstatistical process controlaverage run length

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• Sequential analysis. Tests and confidence intervals
• Optimal stopping times for detecting changes in distributions
• Non-negative matrices and Markov chains. 2nd ed
• Efficient Design and Sensitivity Analysis of Control Charts Using Monte Carlo Simulation
• The Distribution of the Run Length of One-Sided CUSUM Procedures for Continuous Random Variables
• On a unified approach to the analysis of two-sided cumulative sum control schemes with headstarts
• Analysis of CUSUM and Other Markov-Type Control Schemes by Using Empirical Distributions
• Performance of CUSUM Control Schemes for Serially Correlated Observations
• The Effect of Serial Correlation on the Performance of CUSUM Tests
• Comparative performance analysis of the Cumulative Sum chart and the Shiryaev‐Roberts procedure for detecting changes in autocorrelated data
• A novel gradient approach for optimal design and sensitivity analysis of EWMA control charts
• Likelihood Ratio Methods for Monitoring Parameters of a Nested Random Effect Model
• A gradient approach to efficient design and analysis of multivariate EWMA control charts
• An approach to the probability distribution of cusum run length
• A Bayes Approach to a Quality Control Model
• CONTINUOUS INSPECTION SCHEMES

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