对于部分线性模型的乘积调整半参估计

A Semiparametric Estimation with Multiplicative Adjustment for Partially Linear Models

本文使用一种带有乘积调整的半参方法估计部分线性模型的非参数部分并给出所得估计的渐近性质。与传统的非参估计方法相比,我们所使用的半参数方法能够有效的降低所得估计的偏差,而方差不受影响。因此在积分均方误差(MISE)的意义下,该半参数方法要优于传统的估计方法。数值模拟也表明了这一点.

In this paper, we utilize a semiparametric approach with multiplicative adjustment to estimate the nonparametric component of partially linear models. The asymptotic theory and simulation study are discussed. Theoretical results and numerical comparison show that, the semiparametric estimator has the very same large sample variance as the classical estimator, while there is substantial room for reducing the bias. So in the sense of mean integrated squared error （MISE）, the semiparametric method is superior to the classical estimator.