Inclusion principle for statistical inference and learning

Xinjia Chen

doi:10.1117/12.2015055

29 May 2013 Inclusion principle for statistical inference and learning

Xinjia Chen

Proceedings Volume 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI; 875014 (2013) https://doi.org/10.1117/12.2015055
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

In this paper, we propose a general approach for statistical inference and machine learning based on accumulated observational data. We demonstrate that a large class of machine learning problems can be formulated as the general problem of constructing random intervals with pre-specified coverage probabilities for the parameters of the model for the observational data. We show that the construction of such random intervals can be accomplished by comparing the endpoints of random intervals with confidence sequences for the parameters obtained from the observational data. Asymptotic results are obtained for such sequential methods.

Citation Download Citation

Xinjia Chen "Inclusion principle for statistical inference and learning", Proc. SPIE 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI, 875014 (29 May 2013); https://doi.org/10.1117/12.2015055

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