Abstract
The empirical likelihood method is especially useful for constructing confidence intervals or regions of the parameter of interest. This method has been extensively applied to linear regression and generalized linear regression models. In this paper, the empirical likelihood method for single-index regression models is studied. An estimated empirical log-likelihood approach to construct the confidence region of the regression parameter is developed. An adjusted empirical log-likelihood ratio is proved to be asymptotically standard chi-square. A simulation study indicates that compared with a normal approximation-based approach, the proposed method described herein works better in terms of coverage probabilities and areas (lengths) of confidence regions (intervals).
Original language | English |
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Pages (from-to) | 1295-1312 |
Number of pages | 18 |
Journal | Journal of Multivariate Analysis |
Volume | 97 |
Issue number | 6 |
DOIs | |
Publication status | Published - Jul 2006 |
Scopus Subject Areas
- Statistics and Probability
- Numerical Analysis
- Statistics, Probability and Uncertainty
User-Defined Keywords
- Confidence region
- Empirical likelihood
- Single-index model