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|Title:||Large dimensional empirical likelihood||Authors:||Chen, Binbin
Large Dimensional Data
|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
|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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