Department of Mathematics
A profile-type smoothed score function for a varying coefficient partially linear model
The varying coefficient partially linear model is considered in this paper. When the plug-in estimators of coefficient functions are used, the resulting smoothing score function becomes biased due to the slow convergence rate of nonparametric estimations. To reduce the bias of the resulting smoothing score function, a profile-type smoothed score function is proposed to draw inferences on the parameters of interest without using the quasi-likelihood framework, the least favorable curve, a higher order kernel or under-smoothing. The resulting profile-type statistic is still asymptotically Chi-squared under some regularity conditions. The results are then used to construct confidence regions for the parameters of interest. A simulation study is carried out to assess the performance of the proposed method and to compare it with the profile least-squares method. A real dataset is analyzed for illustration. © 2010 Elsevier Inc.
Confidence region, Curse of dimensionality, Local likelihood, Profile-type smoothed score function, Varying coefficient partially linear model
Source Publication Title
Journal of Multivariate Analysis
Link to Publisher's Edition
Li, G., Feng, S., & Peng, H. (2011). A profile-type smoothed score function for a varying coefficient partially linear model. Journal of Multivariate Analysis, 102 (2), 372-385. https://doi.org/10.1016/j.jmva.2010.10.007