Asymptotic normality of randomly truncated stochastic algorithms
Abstract
We study the convergence rate of randomly truncated stochastic algorithms, which consist in the truncation of the standard Robbins-Monro procedure on an increasing sequence of compact sets. Such a truncation is often required in practice to ensure convergence when standard algorithms fail because the expected-value function grows too fast. In this work, we give a self contained proof of a central limit theorem for this algorithm under local assumptions on the expected-value function, which are fairly easy to check in practice.
- Publication:
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arXiv e-prints
- Pub Date:
- March 2010
- DOI:
- 10.48550/arXiv.1003.4183
- arXiv:
- arXiv:1003.4183
- Bibcode:
- 2010arXiv1003.4183L
- Keywords:
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- Mathematics - Probability