TY - JOUR
AU - Mikhail Moklyachuk
AU - Oleksandr Masyutka
AU - Maria Sidei
PY - 2019/01/07
Y2 - 2024/09/15
TI - Extrapolation Problem for Multidimensional Stationary Sequences with Missing Observations
JF - Statistics, Optimization & Information Computing
JA - Stat., optim. inf. comput.
VL - 7
IS - 1
SE - Research Articles
DO - 10.19139/soic.v7i1.527
UR - http://iapress.org/index.php/soic/article/view/2019-M-8
AB - This paper focuses on the problem of the mean square optimal estimation of linear functionals which dependon the unknown values of a multidimensional stationary stochastic sequence. Estimates are based on observations of these quence with an additive stationary noise sequence. The aim of the paper is to develop methods of finding the optimal estimates of the functionals in the case of missing observations. The problem is investigated in the case of spectral certainty where the spectral densities of the sequences are exactly known. Formulas for calculating the mean-square errors and the spectral characteristics of the optimal linear estimates of functionals are derived under the condition of spectral certainty.The minimax (robust) method of estimation is applied in the case of spectral uncertainty, where spectral densities of the sequences are not known exactly while sets of admissible spectral densities are given. Formulas that determine the least favorable spectral densities and the minimax spectral characteristics of the optimal estimates of functionals are proposed for some special sets of admissible densities.
ER -