I'm trying to get my head around profile likelihood for another application and naturally turned to MARK for insight... My problem arises when there is not a 1:1 relation between beta and real parameters. The procedure is clearcut when one wants to obtain an interval for a beta parameter, but what about real parameters? Fixing a real parameter (as required for PLI calculation) then implicitly fixes several betas at once and puts you outside likelihood theory. As I read the MARK help (in my old download), MARK takes an ad hoc approach to 'drag' the ('real') interval near where it should be:
"The method used in MARK to compute the profile likelihood lower bound for parameter i is to minimize the following function:
[-2 log likelihood of current parameter value - (-2 log likelihood of maximum likelihood estimates + c-hat*3.84) ]**2 - (maximum likelihood estimate of parameter i - current parameter value of i).
The first portion of this expression finds the value of the deviance that is c-hat*3.84 units larger than the deviance for the maximum likelihood parameter estimates. The second portion of the expression maximizes the difference between the maximum likelihood parameter estimate and the lower bound." [I'm guessing 'maximizes' is a typo, or maybe there's something I'm missing]
Perhaps it's best to restrict PLI to beta parameters (or, equivalently, real parameters with a 1:1 relation to beta parameters)? I'd appreciate any insights on this, or maybe references to the literature.