Large dimensional empirical likelihood
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 norma...
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| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/88029 http://hdl.handle.net/10220/46882 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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