Estimating Functions and Approximate Conditional Likelihood
将Cox和Reid提出的近似条件似然方法应用于存在多余参数时标量参数Θ的估计，证明基于近似条件似然的估计函数优于基于剖面似然的估计函数，并给出两者等价的充分条件。
The approximate conditional likelihood method proposed by Cox & Reid (1987) is applied to the estimation of a scalar parameter Θ, in the presence of nuisance parameters. The estimating function of Θ based on the approximate conditional likelihood is shown to be preferable to that based on the profile likelihood. A sufficient condition for both approaches to be equivalent is given. The role of parameter orthogonality is emphasized. Several examples including bivariate normal means with known coefficient of variation are presented.