Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model

10 Mar 2015Junwei LuMladen KolarHan Liu

We develop a novel procedure for constructing confidence bands for components of a sparse additive model. Our procedure is based on a new kernel-sieve hybrid estimator that combines two most popular nonparametric estimation methods in the literature, the kernel regression and the spline method, and is of interest in its own right... (read more)

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