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Proceedings Paper

Inclusion principle for statistical inference and learning
Author(s): Xinjia Chen
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Paper 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.

Paper Details

Date Published: 29 May 2013
PDF: 12 pages
Proc. SPIE 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI, 875014 (29 May 2013); doi: 10.1117/12.2015055
Show Author Affiliations
Xinjia Chen, Southern Univ. and A&M College (United States)


Published in SPIE Proceedings Vol. 8750:
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI
Harold H. Szu, Editor(s)

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