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This article is cited in 2 scientific papers (total in 2 papers)
Methods of Signal Processing
On adaptive estimation of linear functionals from observations against white noise
G. K. Golubev Kharkevich Institute for Information Transmission Problems,
Russian Academy of Sciences, Moscow, Russia
Abstract:
We consider the problem of adaptive estimation of a linear functional of an unknown multivariate vector from its observations against white Gaussian noise. As a family of estimators for the functional, we use those generated by projection estimators of the unknown vector, and the main problem is to select the best estimator in this family. The goal of the paper is to explain and mathematically justify a simple statistical idea used in adaptive (i.e., observation-based) choice of the best estimator of a linear functional from a given family of estimators. We also discuss generalizations of the considered statistical model and the proposed estimation method, which allow to cover a broad class of statistical problems.
Keywords:
linear functional, white Gaussian noise, Wiener process, projection estimate, risk envelope, adaptive estimation, Akaike method, soft thresholding, singular value decomposition, spectral regularization.
Received: 14.02.2020 Revised: 25.02.2020 Accepted: 28.02.2020
Citation:
G. K. Golubev, “On adaptive estimation of linear functionals from observations against white noise”, Probl. Peredachi Inf., 56:2 (2020), 95–111; Problems Inform. Transmission, 56:2 (2020), 185–200
Linking options:
https://www.mathnet.ru/eng/ppi2318 https://www.mathnet.ru/eng/ppi/v56/i2/p95
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Abstract page: | 135 | Full-text PDF : | 16 | References: | 13 | First page: | 12 |
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