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Truncated stochastic approximation with moving bounds

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Advanced Monte Carlo Methods for Complex Inference Problems

A wide class of truncated stochastic approximation procedures with moving random bounds will be discussed. This class of procedures has three main characteristics: truncations with random moving bounds, a matrix-valued random step-size sequence, and a dynamically changing random regression function. While we believe that the proposed class of procedures will find its way to a wider range of applications, the main motivation is to accommodate applications to parametric statistical estimation theory. The proposed method allows for incorporation of auxiliary information into the estimation process, and is consistent and asymptotically efficient under certain regularity conditions.

Related Links: – Link to the paper in ArXiv

This talk is part of the Isaac Newton Institute Seminar Series series.

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