A Semiparametric Estimation with Multiplicative Adjustment for Partially Linear Models
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.
NETL
NSTL
万方数据
维普
