Title of article :
Modified estimating functions
Author/Authors :
A.Severini، Thomas نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2002
Abstract :
In a parametric model the maximum likelihood estimator of a parameter of interest (psi) may be viewed as the solution to the equation lʹp ((psi)) = 0, where lp denotes the profile loglikelihood function.It is well known that the estimating function lʹp((psi)) is not unbiased and that this bias can, in some cases, lead to poor estimates of (psi). An alternative approach is to use the modified profile likelihood function, or an approximation to the modified profile likelihood function, which yields an estimating function that is approximately unbiased. In many cases, the maximum likelihood estimating functions are unbiased under more general assumptions than those used to construct the likelihood function, for example under first- or second -moment conditions. Although the likelihood function itself may provide valid estimates under moment conditions alone, the modified profile likelihood requires a full parametric model. In this paper, modifications to lʹp((psi)) are presented that yield an approximately unbiased estimating function under more general conditions.
Keywords :
Generalised linear model , importance sampling , Metropolis–Hastings , Mixture model , Parallel processing , Particle filter , Markov chain Monte Carlo , Batch importance sampling
Journal title :
Biometrika
Journal title :
Biometrika