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Adaptive distributed estimation
26th IEEE Conference on Decision and Control, 1987This paper considers the distributed estimation problem when the communication pattern among the agents is not known a priori. We assume a network of estimation agents which receive measurements from the environment. The estimation agents communicate with each other using schedules which may not be fixed a priori.
Chee-yee Chong +2 more
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Journal of Statistical Planning and Inference, 1984
Abstract Point estimates that are weighted averages of other estimates are considered. They are adaptive because the weights are also functions of the sample observations.In particular, the weights are functions of new measures of peakedness and skewness.
Robert V. Hogg +2 more
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Abstract Point estimates that are weighted averages of other estimates are considered. They are adaptive because the weights are also functions of the sample observations.In particular, the weights are functions of new measures of peakedness and skewness.
Robert V. Hogg +2 more
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Asymptotically Minimax Adaptive Estimation. I: Upper Bounds. Optimally Adaptive Estimates
Theory of Probability & Its Applications, 1992The author presents some new solutions of functional adaptive estimation problems arising in stochastic systems with disturbing parameters affecting the accuracy of estimation. The problems considered include estimation of a signal in a Gaussian white noise, estimation of a functional acting on such a signal, prediction in a polynomial regression with ...
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IEEE Transactions on Acoustics, Speech, and Signal Processing, 1990
AN adaptive algorithm is developed for online estimation of the poles of autoregressive (AR) processes. The method estimates the poles directly from the data without intermediate estimation of the AR coefficients or polynomial factorization. It converges rapidly, is computationally efficient, and attains the Cramer-Rao bound (CRB) asymptotically.
A. Nehorai, D. Starer
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AN adaptive algorithm is developed for online estimation of the poles of autoregressive (AR) processes. The method estimates the poles directly from the data without intermediate estimation of the AR coefficients or polynomial factorization. It converges rapidly, is computationally efficient, and attains the Cramer-Rao bound (CRB) asymptotically.
A. Nehorai, D. Starer
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Optimal adaptive estimation: Structure and parameter adaptation
1970 IEEE Symposium on Adaptive Processes (9th) Decision and Control, 1970Optimal structure and parameter adaptive estimators have been obtained for continuous as well as discrete data gaussian process models with linear dynamics. Specifically, the essentially nonlinear adaptive estimators are shown to be decomposable (partition theorem) into two parts, a linear non-adaptive part consisting of a bank of Kalman-Bucy filters ...
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ICASSP '81. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005
Two basic approaches to adaptive signal processing are in common use. The first and most direct involves substituting data-derived estimates of signal and noise autocorrelations into the standard Wiener-Hopf equation. The second uses a stochastic algorithm, such as the LMS, to minimize the mean square error directly.
S. Sharpe, L. Nolte
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Two basic approaches to adaptive signal processing are in common use. The first and most direct involves substituting data-derived estimates of signal and noise autocorrelations into the standard Wiener-Hopf equation. The second uses a stochastic algorithm, such as the LMS, to minimize the mean square error directly.
S. Sharpe, L. Nolte
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Confidence intervals in adaptive estimation
Journal of Mathematical Sciences, 2000zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bobrov, P. B., Ostrovskij, E. I.
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1995
This paper presents some results concerning the adaptive estimation of density with wavelet methods. We explain three procedures, each one having its own advantages (see [DJKP], [KPT], [TRIB]). The first one is an empirical method based on simulations; the bandwidth is chosen with a cross validated criteria.
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This paper presents some results concerning the adaptive estimation of density with wavelet methods. We explain three procedures, each one having its own advantages (see [DJKP], [KPT], [TRIB]). The first one is an empirical method based on simulations; the bandwidth is chosen with a cross validated criteria.
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