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Teoriya Veroyatnostei i ee Primeneniya, 1995, Volume 40, Issue 1, Pages 192–199
(Mi tvp3344)
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This article is cited in 1 scientific paper (total in 1 paper)
Short Communications
Slow stochastic approximation processes are good for estimating slope
L. Gerencsér Systems and Control Laboratory, Computer and Automation Institute Hungarian Academy of Sciences, Budapest, Hungary
Abstract:
A continuous-time Robbins–Monroe process violating the conditions necessary for the CLT to hold will be considered. It will be shown that although the estimator process 6% converges to the root to be determined a.s. it is sufficiently rich to get strong consistent estimator of the slope of the regressor function using noisy observations of the regressor function at $\theta_t-s$ only.
Keywords:
stochastic approximation, least square estimation, stochastic regression, the Lai–Wei condition, the Cameron–Martin formula.
Received: 26.08.1993
Citation:
L. Gerencsér, “Slow stochastic approximation processes are good for estimating slope”, Teor. Veroyatnost. i Primenen., 40:1 (1995), 192–199; Theory Probab. Appl., 40:1 (1995), 145–151
Linking options:
https://www.mathnet.ru/eng/tvp3344 https://www.mathnet.ru/eng/tvp/v40/i1/p192
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Abstract page: | 370 | Full-text PDF : | 57 | First page: | 12 |
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