Abstract

The problem of optimal estimation of the linear functionals $A{\zeta}=\sum_{j=1}^{\infty}{a}(j){\zeta}(j),$ which depend on the unknown values of a periodically correlated stochastic sequence ${\zeta}(j)$ from observations of the sequence ${\zeta}(j)+{\theta}(j)$ at points $j\in\{...,-n,...,-2,-1,0\}\setminus S$, $S=\bigcup _{l=1}^{s-1}\{-M_l\cdot T+1,\dots,-M_{l-1}\cdot T-N_{l}\cdot T\}$, is considered, where ${\theta}(j)$ is an uncorrelated with ${\zeta}(j)$ periodically correlated stochastic sequence. Formulas for calculation the mean square error and the spectral characteristic of the optimal estimate of the functional $A\zeta$ are proposed in the case where spectral densities of the sequences are exactly known. Formulas that determine the least favorable spectral densities and the minimax-robust spectral characteristics of the optimal estimates of functionals are proposed in the case of spectral uncertainty, where the spectral densities are not exactly known while some sets of admissible spectral densities are specified.

Highlights

  • In this article we study the extrapolation of the functionals Aζ and AN ζ which depend on the unobserved values of a periodically correlated stochastic sequence ζ(j)

  • Estimates are based on observations of a periodically correlated stochastic sequence ζ(j) + θ(j) with missing observations, that means that observations of ζ(j) +

  • The extrapolation problem is considered under the condition of spectral certainty and under the condition of spectral uncertainty

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Summary

Introduction

Formulation and investigation of the problems of extrapolation, interpolation and filtering of linear functionals which depend on the unknown values of stationary sequences and processes from observations with and without noise are presented by M.P. Moklyachuk [22], [23]. In their book M.M Luz and M.P. Moklyachuk [16] presented results of investigation of the minimax estimation problems for linear functionals which depends on unknown values of stochastic sequence with stationary increments. I.I. Golichenko and M.P. Moklyachuk [2], [3], [4], [24] investigated the interpolation, extrapolation and filtering problems of linear functionals from periodically correlated stochastic sequences and processes. Formulas that determine the least favorable spectral densities and the minimaxrobust spectral characteristics of the optimal estimates of functionals are proposed in the case of spectral uncertainty, where the spectral densities are not exactly known while some sets of admissible spectral densities are specified

Periodically correlated and multidimensional stationary sequences
The classical projection method of linear extrapolation
Conclusions
New York
Full Text
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