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Recursive parameter estimation procedures

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If you have a question about this talk, please contact Rachel Fogg .

Parameter estimation procedures will be discussed for some important classes of statistical models using ideas of stochastic approximation theory. Stochastic approximation is a method to locate a root of an unknown function when only noisy measurements of the function can be observed. These procedures naturally allow for on-line implementation and do not require storing all the data, which is particularly convenient for sequential data processing. In particular, new procedures for estimating autoregressive parameters in $AR(m)$ models will be considered. The proposed method allows for incorporation of auxiliary information into the estimation process, and is consistent and asymptotically efficient under certain regularity conditions.

This talk is part of the Probabilistic Systems, Information, and Inference Group Seminars series.

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