Skip to main content
SpringerLink
Log in

Increasing the Robustness of Estimators of the State of Time and Frequency Standards

  • TIME AND FREQUENCY MEASUREMENTS
  • Published:
Measurement Techniques Aims and scope

Causes for the appearance of instability in estimators of the state of time and frequency standards found on the basis of the results of measurements are considered. Methods of increasing the robustness of statistical estimators obtained based on the use of adaptive predictive models as well as in regimes of static and dynamic data processing are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Yu. P. Khrustalev et al., “Processing of data obtained from the results of mutual measurements of a secondary time and frequency standard,” Vest. Irkut. Gos. Tekh. Univ., No. 7, 22–29 (2012).

    Google Scholar 

  2. Yu. P. Khrustalev, “Statistical and dynamic processing of data obtained when handling time and frequency standards,” Izmer. Tekhn., No. 6, 20–32 (2004); Measur. Techn., 47, No. 6, 555–561 (2004).

  3. V. P. Borovikov, A Popular Introduction to Modern Data Analysis in the STATISTICA System, Goryachaya Liniya. Telekom, Moscow (2013).

    Google Scholar 

  4. F. R. Gantmakher, Matrix Theory, Nauka, Moscow (1968).

    Google Scholar 

  5. R. L. Loner and G. N. Wilkson, Robust Statistical Methods of Data Estimation [Russian translation], Mashinostroenie, Moscow (1984).

    Google Scholar 

  6. V. A. Okhorzin, Computer Modeling in the MATHCAD System, Financy i Statistika, Moscow (2006).

    Google Scholar 

  7. K. K. Paskal et al., “A study of processes in the construction of models of group standards,” Vest. Irkut. Gos. Tekh. Univ., No. 4, 29–34 (2013).

    Google Scholar 

  8. F. Hampel, Robustness in Statistics [Russian translation], Mir, Moscow (1989).

    Google Scholar 

  9. B. P. Filimonov and Yu. P. Khrustalev, “Determination of the frequencies of quantum-mechanical frequency standards by adaptive methods,” Izmer. Tekhn., No. 7, 27–30 (1987); Measur. Techn., 30, No. 1, 43–49 (1987).

  10. D. Himmelblau, Applied Nonlinear Programming [Russian translation], Mir, Moscow (1975).

    Google Scholar 

  11. J. Box and G. Jenkins, Analysis of Time Series, Prediction, and Control [Russian translation], Mir, Moscow (1974).

    Google Scholar 

  12. Yu. M. Ermol’ev, Methods of Stochastic Programming, Nauka, Moscow (1976).

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Irkutsk State Technical University, Irkutsk, Russia

    Yu. P. Khrustalev & I. A. Serysheva

Authors
  1. Yu. P. Khrustalev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. I. A. Serysheva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yu. P. Khrustalev.

Additional information

Translated from Izmeritel’naya Tekhnika, No. 5, pp. 29–34, May, 2014.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khrustalev, Y.P., Serysheva, I.A. Increasing the Robustness of Estimators of the State of Time and Frequency Standards. Meas Tech 57, 519–525 (2014). https://doi.org/10.1007/s11018-014-0490-4

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11018-014-0490-4

Keywords

Search

Navigation