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An empirical likelihood-based method for comparison of treatment effects--Test of equality of coefficients in linear models

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  • Su, Haiyan
  • Liang, Hua

Abstract

To compare two treatment effects, which can be described as the difference of the parameters in two linear models, we propose an empirical likelihood-based method to make inference for the difference. Our method is free of the assumptions of normally distributed and homogeneous errors, and equal sample sizes. The empirical likelihood ratio for the difference of the parameters of interest is shown to be asymptotically chi-squared. Simulation experiments illustrate that our method outperforms the published ones. Our method is used to analyze a data set from a drug study.

Suggested Citation

  • Su, Haiyan & Liang, Hua, 2010. "An empirical likelihood-based method for comparison of treatment effects--Test of equality of coefficients in linear models," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1079-1088, April.
  • Handle: RePEc:eee:csdana:v:54:y:2010:i:4:p:1079-1088
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    References listed on IDEAS

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    1. Schmidt, Peter & Sickles, Robin, 1977. "Some Further Evidence on the Use of the Chow Test under Heteroskedasticity," Econometrica, Econometric Society, vol. 45(5), pages 1293-1298, July.
    2. Jayatissa, W A, 1977. "Tests of Equality between Sets of Coefficients in Two Linear Regressions when Disturbance Variances Are Unequal," Econometrica, Econometric Society, vol. 45(5), pages 1291-1292, July.
    3. Song Xi Chen & Hengjian Cui, 2006. "On Bartlett correction of empirical likelihood in the presence of nuisance parameters," Biometrika, Biometrika Trust, vol. 93(1), pages 215-220, March.
    4. Liu W. & Jamshidian M. & Zhang Y., 2004. "Multiple Comparison of Several Linear Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 395-403, January.
    5. Weerahandi, Samaradasa, 1987. "Testing Regression Equality with Unequal Variances," Econometrica, Econometric Society, vol. 55(5), pages 1211-1215, September.
    6. Hua Liang & Suojin Wang & Raymond J. Carroll, 2007. "Partially linear models with missing response variables and error-prone covariates," Biometrika, Biometrika Trust, vol. 94(1), pages 185-198.
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    Cited by:

    1. Wei Yu & Cuizhen Niu & Wangli Xu, 2014. "An empirical likelihood inference for the coefficient difference of a two-sample linear model with missing response data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(5), pages 675-693, July.

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