Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents Abstract. Partial likelihood. COX D. Department of Mathematics, Imperial College. Oxford Academic. Google Scholar. Revision received:. Cite Cite D. Select Format Select format. Permissions Icon Permissions. Abstract A definition is given of partial likelihood generalizing the ideas of conditional and marginal likelihood. Issue Section:. You do not currently have access to this article.
Download all slides. Sign in Don't already have an Oxford Academic account? You could not be signed in. Sign In Forgot password? Don't have an account? Sign in via your Institution Sign in. For example, in linear regression, interest typically lies in the slope coefficients and not on the error variance. All three are used when dealing with nuisance parameters in the completely specified likelihood function. The marginal likelihood is the primary method to eliminate nuisance parameters in theory.
It's a true likelihood function i. The partial likelihood is not a true likelihood in general. However, in some cases it can be treated as a likelihood for asymptotic inference. Efron showed that the partial likelihood loses little to no information for a variety of hazard functions.
The profile likelihood is convenient when we have a multidimensional likelihood function and a single parameter of interest.
This can work well in practice, though there is a potential bias in the MLE obtained in this way; the marginal likelihood corrects for this bias. Sign up to join this community. The best answers are voted up and rise to the top.
Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Learn more. What is the difference between a partial likelihood, profile likelihood and marginal likelihood?
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