A Semiparametric Maximum Likelihood Estimator
提出一种半参数最大似然估计方法，用于处理参数向量包含有限维和非参数成分的模型，无需对非参数部分建模，估计量具有√N一致性和渐近正态性，并达到半参数效率界。
This paper presents a procedure for analyzing a model in which the parameter vector has two parts: a finite-dimensional component 8 and a nonparametric component A. The procedure does not require parametric modeling of λ but assumes that the true density of the data satisfies an index restriction. The idea is to construct a parametric model passing through the true model and to estimate θ by setting the score for the parametric model to zero. The score is estimated nonparametrically and the estimator is shown to be √N consistent and asymptotically normal. The estimator is then shown to attain the semiparametric efficiency bound characterized in Begun et al. (1983) for multivariate nonlinear regression, simultaneous equations, partially specified regression, index regression, censored regression, switching regression, and disequilibrium models in which the error densities are unknown.