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Profile likelihood for estimation and confidence intervals

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  • Patrick Royston

    (Cancer and Statistical Methodology Groups, MRC Clinical Trials Unit)

Abstract

Normal-based confidence intervals for a parameter of interest are inaccurate when the sampling distribution of the estimate is nonnormal. The technique known as profile likelihood can produce confidence intervals with better coverage. It may be used when the model includes only the variable of interest or several other variables in addition. Profile-likelihood confidence intervals are particularly useful in nonlinear models. The command pllf computes and plots the maximum likelihood estimate and profile likelihood-based confidence interval for one parameter in a wide variety of regression models. Copyright 2007 by StataCorp LP.

Suggested Citation

  • Patrick Royston, 2007. "Profile likelihood for estimation and confidence intervals," Stata Journal, StataCorp LP, vol. 7(3), pages 376-387, September.
    • Handle: RePEc:tsj:stataj:v:7:y:2007:i:3:p:376-387
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    References listed on IDEAS

    as
    1. W. Sauerbrei & P. Royston, 1999. "Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(1), pages 71-94.
    2. Patrick Royston, 2001. "Flexible alternatives to the Cox model, and more," Stata Journal, StataCorp LP, vol. 1(1), pages 1-28, November.
    3. Patrick Royston & Gareth Ambler, 1999. "Multivariable fractional polynomials," Stata Technical Bulletin, StataCorp LP, vol. 8(43).
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    Cited by:

    1. Thomas Kopp, 2022. "When switching costs cause market power: Rubber processing in Indonesia," Agricultural Economics, International Association of Agricultural Economists, vol. 53(3), pages 481-495, May.

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