Statistical Stability Analysis of Stationary Markov Models
Keywords:
Statistical Stability, Quasi-Homogeneous Model, Statistical Volatility, Random Walk, Markov Chain, Complex Technical System, Accuracy, Transition Probability, Number of Process Implementations, Model DivergenceAbstract
An approach is proposed to assess the quality of stationary Markov models without absorbing states on the basis of a measure of statistical stability: the description is formulated and its properties are determined. It is shown that the estimates of statistical stability of models were raised by different authors, either as a methodological aspect of the model quality, or within the framework of other model properties. When solving practical problems of simulation, for example, based on Markov models, there is a pronounced problem of ensuring the dimension of the required samples. On the basis of the introduced formulations, a constructive approach to solving the problems of sample size optimization and statistical volatility analysis of the Markov model to the emerging anomalies with restrictions on the accuracy of the results is proposed, which ensures the required reliability and the exclusion of non-functional redundancy.
To analyze the type of transitions in the transition matrix, a measure of its divergence (normalized and centered) is introduced. This measure does not have the completeness of the description and is used as an illustrative characteristic of the models of a certain property. The estimation of the divergence of transition matrices can be useful in the study of models with high sensitivity of detection of the studied properties of objects. The key stages of the approach associated with the study of quasi-homogeneous models are formulated.
Quantitative estimates of statistical stability and statistical volatility of the model are proposed on the example of modeling a real technical object with failures, recovery and prevention. The effectiveness of the proposed approaches in solving the problem of statistical stability analysis in the problems of qualimetric analysis of quasi-homogeneous models of complex systems is shown. On the basis of the offered constructive approach the operational tool of decision-making on parametric and functional adjustment of difficult technical objects on long-term and short-term prospects is received.
References
2. Panella I., Hardwick G. Model Oriented System Design Applied to Commercial Aircraft Secondary Flight Control Systems. International Conference Simulation and Modeling Methodologies, Technologies and Applications. 2017. pp. 55–76.
3. Boev V.D. Issledovanie adekvatnosti GPSS World i AnyLogic pri modelirovanii diskretno-sobytijnyh processov: Monografiya [Study of the adequacy of GPSS World and AnyLogic in the modeling of discrete-event processes]. SPb: VAS. 2011. 404 p. (In Russ.).
4. Skatkov A.V., Balakireva I.A. [Ensuring functional stability of operating characteristics of environmental monitoring systems at arbitrary input data flow]. Sistemy kontrolja okruzhajushhej sredy – Monitoring systems of environment]. 2017. vol. 8(28) pp. 47–54. (In Russ.).
5. Laaksonen O., Peltoniemi M. The essence of dynamic capabilities and their measurement. International Journal of Management Reviews. 2018. vol. 20(2). pp. 184–205.
6. Langville A.N., Meyer C.D. Updating Markov chains with an eye on Google’s PaGeRank. SIAM Journal on Matrix Analysis and Applications. 2006. vol. 27(4). рр. 968–987.
7. Musaev A.A., Skvorcov M.S. [Methods of parametric optimization of reliability of structurally complex technical systems]. Trudy SPIIRAN – SPIIRAS Proceedings. 2008. vol. 6. pp. 44–50. (In Russ.).
8. Li Y.F., Zio E. A multi-state model for the reliability assessment of a distributed generation system via universal generating function. Reliability Engineering & System Safety. 2012. vol. 106. pp. 28–36.
9. Chen N., Majda A. Conditional Gaussian systems for multiscale nonlinear stochastic systems: prediction, state estimation and uncertainty quantification. Entropy. 2018. vol. 20. no. 7. pp. 1–80.
10. Kondrashkov A.V., Pichugin Ju.A. [Identification and statistical verification of Volterra model stability]. Nauchno-tehnicheskie vedomosti SPbGPU. Fiziko-matematicheskie nauki – St. Petersburg Polytechnic University Journal. Physics and Mathematics. 2014. vol. 1(189). pp. 124–135. (In Russ.).
11. Garza-Reyes J.A. et al. A PDCA-based approach to Environmental Value Stream Mapping (E-VSM). Journal of Cleaner Production. 2018. vol. 180. pp. 335–348.
12. Gorban I.I. The Statistical Stability Phenomenon. Springer. 2017. 361 p.
13. Mishura I.S. Stohastic Calculus for Fractorial Brownian Motion and Related Processes. Springer. 2008. 393 p.
14. Mikoni S.V., Sokolov B.V. Jusupov R.M. Kvalimetrija modelej i polimodel'nyh kompleksov: monografija [Qualimetry of models and polydivide complexes: monograph]. M.: RAN. 2018. 314 p. (In Russ.).
15. Sokolov B.V., Yusupov R.M. Information Fusion Models’ Quality Estimation And Models’ Quality Control Theory. VI ISTC Scientific Advisory Committee Seminar Science and Computing. 2003. pp. 102–104.
16. Rostovcev Ju.G., Jusupov R.M. [The problem of ensuring the adequacy of subject-object modeling]. Izvestiya vysshikh uchebnykh zavedeniy. Priborostroenie – Journal of Instrument Engineering. 1991. Issue 24. vol. 7. pp. 7–14. (In Russ.).
17. Sovetov B.Ja. et al. Imitacionnoe modelirovanie sistem [Simulation modeling of systems]. Petrodvorec: VUNC VMF. 2010. 343 p. (In Russ.).
18. Dolgui A., Ivanov D., Sokolov B. Scheduling of recovery action in the supply chain with resilience analysis consideration. International Journal of Production Research. 2018. vol. 56. no. 19. pp. 6473–6490.
19. Zhao Y. et al. Fast noisy image quality assessment based on free-energy principle. Communications in Computer and Information Science. 2018. vol. 815. pp. 290–299.
20. Okhtilev M.Yu. et al. Methods and Algorithms of Integrated Modeling of Complex Technical Objects in Dynamically Changing Conditions. Proceedings of the International Scientific Conference ММEТ NW. 2018. pp. 282–284.
21. Sokolov B. et al. Logic Dynamic Model And Algorithms Of Operation Complex. European Modeling & Simulation Symposium (EMSS-2018). 2018. pp. 59–67.
22. Degiannakis S., Floros C. Methods of Volatility Estimation and Forecasting. In: Modelling and Forecasting High Frequency Financial Data. Palgrave Macmillan. 2015. pp. 58–109.
23. Gerasimova D.S., Sayapin A.V., Palukhin A.A., Katsura A.V. [Application of the bootstrap method for statistical characteristics assessment of aircraft components’ small samples]. Sibirskij zhurnal nauki i tekhnologij – Siberian Journal of Science and Technology. 2018. vol. 3. pp. 482–488. Available at: https://cyberleninka.ru/article/n/application-of-the-bootstrap-method-for-statistical-characteristics-assessment-of-aircraft-components-small-samples (accessed: 24.03.2019).
24. Park K.I. Fundamentals of Probability and Stochastic Processes with Applications to Communications. Springer International Publishing. 2018. 273 p.
25. Tien I., Der Kiureghian A. Algorithms for bayesian network modeling and reliability assessment of infrastructure systems. Reliability Engineering & System Safety. 2016. vol. 156. pp. 134–147.
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