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Automation and Remote Control

, Volume 62, Issue 9, pp 1453–1476 | Cite as

Nonparametric Estimation of the Logarithmic Density Derivative of Sequences with Strong Mixing

  • A. V. Dobrovidov
  • G. M. Koshkin
Article

Abstract

In nonparametric signal estimation, there is a need for estimating the logarithmic probability density derivatives. This problem is complex, because the logarithmic density derivative is a function with singularity—a ratio containing density in the denominator. Since the density estimate can take values close or even equal to zero, the estimate of the logarithmic derivative becomes unstable. This difficulty is surmounted by constructing a new nonparametric estimate for the logarithmic derivative, i.e., an estimate that is stable to observation and based piecewise-smooth approximation. Its properties for dependent observations generated by stationary processes satisfying the strong mixing condition are studied. The rate of convergence of the nonparametric estimate and the principal part of the expansion of the mean-square estimate error are determined.

Keywords

Probability Density Estimate Error Stationary Process System Theory Density Estimate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© MAIK “Nauka/Interperiodica” 2001

Authors and Affiliations

  • A. V. Dobrovidov
    • 1
  • G. M. Koshkin
    • 1
  1. 1.Trapeznikov Institute of Control SciencesRussian Academy of SciencesMoscowRussia

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