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Empirical likelihood for linear models in the presence of nuisance parameters

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  • Kim, Mi-Ok
  • Zhou, Mai

Abstract

We propose a simple alternative empirical likelihood (EL) method in linear regression which requires the same conditions of the ordinary profile EL but overcomes the challenge of maximizing the likelihood in the presence of high dimensional nuisance parameters. We adapt the idea of added variable plots. We regress the response and the independent variables of main interest on the ancillary variables and construct the likelihood based on the residuals. The hence constructed EL ratio has constraints only pertaining to the parameters of interest and has a standard [chi]2 limiting distribution. Numerical results are included.

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  • Kim, Mi-Ok & Zhou, Mai, 2008. "Empirical likelihood for linear models in the presence of nuisance parameters," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1445-1451, September.
    • Handle: RePEc:eee:stapro:v:78:y:2008:i:12:p:1445-1451
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    References listed on IDEAS

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    1. Chen, S. X., 1994. "Empirical Likelihood Confidence Intervals for Linear Regression Coefficients," Journal of Multivariate Analysis, Elsevier, vol. 49(1), pages 24-40, April.
    2. Steven E. Stern, 1997. "A Second‐order Adjustment to the Profile Likelihood in the Case of a Multidimensional Parameter of Interest," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(3), pages 653-665.
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