Interpolation Problem for Multidimensional Stationary Processes with Missing Observations
| Autor: | Oleksandr Masyutka, Maria Sidei, Mikhail Moklyachuk |
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| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Control and Optimization Stationary process Mean squared error Process (computing) Stationary process optimal estimate mean square error minimax-robust estimate least favorable spectral density minimax spectral characteristic Minimax Noise (electronics) Linear estimation Artificial Intelligence Signal Processing Applied mathematics Computer Vision and Pattern Recognition lcsh:Probabilities. Mathematical statistics Statistics Probability and Uncertainty lcsh:QA273-280 Real line Information Systems Interpolation Mathematics |
| Zdroj: | Statistics, Optimization and Information Computing, Vol 7, Iss 1, Pp 118-132 (2019) |
| ISSN: | 2310-5070 2311-004X |
| DOI: | 10.19139/soic.v7i1.430 |
| Popis: | The problem of the mean-square optimal linear estimation of linear functionals which depend on the unknown values of a multidimensional continuous time stationary stochastic processis considered.Estimates are based on observations of the process with an additive stationary stochastic noise process at points which do not belong to some finite intervals of a real line. The problem is investigated in the case of spectral certainty, where the spectral densities of the processes are exactly known. Formulas for calculating the mean-square errors and spectral characteristics of the optimal linear estimates of functionals are proposed 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 processes are not known exactly while some sets of admissible spectral densities of the processes 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 spectral densities. |
| Databáze: | OpenAIRE |
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