Name
Abstract | ||
|---|---|---|
Partially linear models with local kernel regression are popular non-parametric techniques. However, bandwidth selection in the models is a puzzling topic that has been addressed in literature with the use of undersmoothing and regular smoothing. In an attempt to address the strategy of bandwidth selection, we review profile-kernel based and backfitting methods for partially linear models, and justify why undersmoothing is necessary for backfitting method and why the "optimal" bandwidth works out for profile-kernel based method. We suggest a general computation strategy for estimating nonparametric functions. We also employ the penalized spline method for partially linear models and conduct intensive simulation experiments to explore the numerical performance of the penalized spline method, profile and backfitting methods. A real example is analyzed with the three methods. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.csda.2004.10.007 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
popular nonparametric technique,linear model,bandwidth selection,backfitting method,numerical comparison,penalized spline method,local kernel regression,intensive simulation experiment,nonparametric function,general computation strategy,numerical performance,kernel regression,mixed effects model,simulation experiment | Econometrics,Linear model,Nonparametric regression,Model selection,Nonparametric statistics,Smoothing,Kernel method,Statistics,Backfitting algorithm,Mathematics,Kernel regression | Journal |
Volume | Issue | ISSN |
50 | 3 | 0167-9473 |
Citations | PageRank | References |
7 | 3.23 | 0 |
Authors | ||
1 | ||