Profile likelihood for estimation and confidence intervals
Patrick Royston
Cancer and Statistical Methodology Groups
MRC Clinical Trials Unit
London, UK
[email protected]
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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.
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Patrick Royston
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pllf, profile likelihood, confidence interval, nonnormality, nonlinear model
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