• Title of article

    Modified estimating functions

  • Author/Authors

    A.Severini، Thomas نويسنده ,

  • Issue Information
    روزنامه با شماره پیاپی سال 2002
  • Pages
    -332
  • From page
    333
  • To page
    0
  • 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
  • Serial Year
    2002
  • Journal title
    Biometrika
  • Record number

    71807