Smooth backfitting in practice

By Jens Perch Nielsen, Stefan Sperlich. 2005

99 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/77215d06019ee89b1120f2d33fdaac68e76ff11a

How researchers see this paper

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

In Management Science and Operations Research

1. Liangjun Su
2. Oliver B. Linton
3. Lijian Yang
4. Byeong U. Park
5. Degui Li
6. Xun Lu
7. Aman Ullah
8. Jing Wang

In Statistics and Probability

1. Liangjun Su
2. Enno Mammen
3. Byeong U. Park
4. Lijian Yang
5. Degui Li
6. Aman Ullah
7. Xun Lu
8. Zudi Lu

Citations in the corpus

• A flexible semiparametric forecasting model for time series (2015, methodology): “As one referee suggested, for the additive modelling, we also considered the smooth backfitting method due to Mammen et al (1999) and Nielsen and Sperlich (2005) with the R package sBF.”
• SPLINE-BACKFITTED KERNEL SMOOTHING OF ADDITIVE COEFFICIENT MODEL (2010, background): “Other alternatives for estimating model (1) that may satisfy criteria (i)–(iii) are possible extensions of the smoothed backfitting of Mammen, Linton, and Nielsen (1999) and Nielsen and Sperlich (2005), and the two-stage estimator of Horowitz and Mammen (2004).”
• Nonparametric dynamic panel data models: Kernel estimation and specification testing (2013, methodology): “, Nielsen and Sperlich (2005), Henderson, Carroll and Li (2008), and MST (2009).”
• Efficient and fast spline-backfitted kernel smoothing of additive models (2009, methodology): “Mammen et al. (1999 )p roposed a modified backfitting algorithm with nice theoretical properties, which was implemented in Nielsen and Sperlich (2005) and called smooth backfitting estimator.”
• Smooth backfitting in generalized additive models (2008, methodology): “This was illustrated by very convincing simulations in Nielsen and Sperlich [20], where also surprisingly good performance of smooth backfitting was reported for very high dimensions.”
• Local polynomial estimation of nonparametric simultaneous equations models (2008, methodology): “For example, Nielsen and Sperlich (2005) apply the leave-one-out least squares crossvalidation (LSCV) in the smooth backfitting context.”
• Estimation of a semiparametric transformation model (2008, methodology): “Our implementations for estimators of the additive index follow exactly Nielsen and Sperlich (2005) for the backfitting (BF), and Hengartner and Sperlich (2005) for the marginal integration (MI).”