Efficient and fast spline-backfitted kernel smoothing of additive models

By Jing Wang, Lijian Yang. 2009

49 citations recorded by the source corpus.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e905c529eb28fe519901ad5ae491caee788e3394

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

1. Jane-ling Wang
2. Byeong U. Park
3. Peter X.-K. Song
4. Xiaoke Zhang
5. Shujie Ma
6. H. Muller
7. Jeng-Min Chiou
8. Fang Yao

Citations in the corpus

• Varying Index Coefficient Models (2015, methodology): “This two-step estimation approach is also used in Wang and Yang (2007), Wang and Yang (2009b), and Liu and Yang (2010), in which they use piecewise constant or linear splines in the first step of splines estimation.”
• Time-Varying Additive Models for Longitudinal Data (2013, background): “Wang and Yang (2007) and Wang and Yang (2009) overcame this theoretical hurdle by adding a second stage to backfit the spline estimator with a kernel smoother so that the two-stage spline-backfitted kernel estimator is asymptotically tractable and retain the oracle property for both the bias and…”
• Spline-backfitted kernel smoothing of partially linear additive model (2011, unknown): “A major theoretical innovation is to resolve the dependence between T and X, making use of Assumption (A5), which is not needed in Wang and Yang (2007).”