Efficient estimation of semiparametric models by smoothed maximum likelihood
Efficient estimation of semiparametric models by smoothed maximum likelihood
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DOI：
10.1111/j.1468-2354.2007.00461.x
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发表时间：
2007-11-01
影响因子：
1.5
通讯作者：
Cosslett, Stephen R.
中科院分区：
文献类型：
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作者：
Cosslett, Stephen R.
对于含有未知密度函数和参数指数函数的半参数模型，利用光滑似然函数构造了有效的估计量。使可能性平滑使得关于未知密度函数的最大化更容易处理。该方法被用来展示在三种情况下从总体份额知识中获得的效率收益：(1)二元选择；(2)只对一个结果进行抽样时的二元选择，并补充解释变量的随机抽样；(3)线性回归，其中份额由因变量的阈值定义。对于参数分量和一类误差密度泛函，都获得了半参数效率。
A smoothed likelihood function is used to construct efficient estimators for some semiparametric models that contain unknown density functions together with parametric index functions. Smoothing the likelihood makes maximization with respect to the unknown density functions more tractable. The method is used to show the efficiency gains from knowledge of population shares in three cases: (1) binary choice; (2) binary choice when only one outcome is sampled, supplemented by random sampling of the explanatory variables; and (3) linear regression, where the shares are defined by a threshold value of the dependent variable. Semiparametric efficiency is achieved both for parametric components and for a class of functionals of the error density.