  In this paper, utilizing recent theoretical results in high dimensional
statistical modeling, we propose a model-free yet computationally simple
approach to estimate the partially linear model $Y=X\beta+g(Z)+\varepsilon$.
Motivated by the partial consistency phenomena, we propose to model $g(Z)$ via
incidental parameters. Based on partitioning the support of $Z$, a simple local
average is used to estimate the response surface. The proposed method seeks to
strike a balance between computation burden and efficiency of the estimators
while minimizing model bias. Computationally this approach only involves least
squares. We show that given the inconsistent estimator of $g(Z)$, a root $n$
consistent estimator of parametric component $\beta$ of the partially linear
model can be obtained with little cost in efficiency. Moreover, conditional on
the $\beta$ estimates, an optimal estimator of $g(Z)$ can then be obtained
using classic nonparametric methods. The statistical inference problem
regarding $\beta$ and a two-population nonparametric testing problem regarding
$g(Z)$ are considered. Our results show that the behavior of test statistics
are satisfactory. To assess the performance of our method in comparison with
other methods, three simulation studies are conducted and a real dataset about
risk factors of birth weights is analyzed.
