Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88029
Title: Large dimensional empirical likelihood
Authors: Chen, Binbin
Pan, Guangming
Yang, Qing
Zhou, Wang
Keywords: Empirical Likelihood
Large Dimensional Data
DRNTU::Science::Mathematics
Issue Date: 2015
Source: Chen, B., Pan, G., Yang, Q., & Zhou, W. (2015). Large dimensional empirical likelihood. Statistica Sinica, 25, 1659-1677. doi:10.5705/ss.2013.246
Series/Report no.: Statistica Sinica
Abstract: The empirical likelihood is a versatile nonparametric approach to testing hypotheses and constructing confidence regions. However it is not clear if Wilks’ Theorem still works in high dimensions. In this paper, by adding two pseudo-observations to the original data set, we prove the asymptotic normality of the log empirical likelihood-ratio statistic when the sample size and the data dimension are comparable. In practice, we suggest using the normalized F(p,n-p) distribution to approximate its distribution. Simulation results show excellent performance of this approximation.
URI: https://hdl.handle.net/10356/88029
http://hdl.handle.net/10220/46882
ISSN: 1017-0405
DOI: http://dx.doi.org/10.5705/ss.2013.246
Rights: © 2015 Statistica Sinica.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SPMS Journal Articles

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