Filtering Problem for Stationary Sequences with Missing Observations
| Autor: | Maria Sidei, Mikhail Moklyachuk |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Mathematical optimization Sequence Control and Optimization Mean squared error Maximum entropy spectral estimation Stationary sequence Minimax Stationary sequence mean square error minimax-robust estimate least favorable spectral density minimax spectral characteristic Linear estimation Artificial Intelligence Signal Processing Filtering problem Applied mathematics Computer Vision and Pattern Recognition Statistics Probability and Uncertainty lcsh:Probabilities. Mathematical statistics lcsh:QA273-280 Information Systems Mathematics Stationary noise |
| Zdroj: | Statistics, Optimization and Information Computing, Vol 4, Iss 4, Pp 308-325 (2016) |
| ISSN: | 2310-5070 |
| Popis: | We deal with the problem of the mean-square optimal linear estimation of linear functionals which depend on the unknown values of a stationary stochastic sequence from observations of the sequence with a stationary noise sequence. 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, where spectral densities of the sequences are exactly known. The minimax (robust) method of estimation is applied in the case of spectral uncertainty, where spectral densities 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 are proposed for some special sets of admissible densities. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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