Derivative Estimation and Testing in Generalized Additive Models

By Lijian Yang, S. Sperlich, Wolfgang Karl Härdle. 2003

220 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/511baceddd5faac5626b5ae908a187a6f60ffa88

How researchers see this paper

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

In Artificial Intelligence

1. Steve Horvath
2. Peter Langfelder
3. Johanna S. Hardin
4. David Golan
5. Stephan Ramon Garcia
6. Bin Zhang

In Statistics and Probability

1. Steve Horvath
2. Johanna S. Hardin
3. Bin Zhang
4. David Golan
5. Stephan Ramon Garcia
6. Peter Langfelder

Citations in the corpus

• Oracally Efficient Two-Step Estimation of Generalized Additive Model (2013, unknown): “…to the additive model (1), see, for instance, the B-spline method of Stone (1986) and Xue and Liang (2010); the kernel method of Linton and Härdle (1996) and Yang, Sperlich, and Härdle (2003); and the two-stage methods of Horowitz and Mammen (2004) and Horowitz, Klemelä, and Mammen (2006).”
• Local polynomial estimation of nonparametric simultaneous equations models (2008, unknown): “See Opsomer and Ruppert (1998) in the back-fitting context, and Severance-Lossin and Sperlich (1999), Sperlich et al. (1999), and Yang et al. (2003) for derivative estimation and testing in the marginal integration context.”
• Nucleosome-Interacting Proteins Regulated by DNA and Histone Methylation (2010, methodology): “Clustering was performed in R using the hopach package (van der Laan and Pollard, 2003).”
• Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R (2008, methodology): “Many clustering procedures have been developed for the analysis of microarray data (Dembele and Kastner, 2003; Ghosh and Chinnaiyan, 2002; Thalamuthu et al., 2006; van der Laan and Pollard, 2003).”
• A method for generating realistic correlation matrices (2011, methodology): “, hierarchical clustering, PAM [22], HOPACH [40], 43 QT clustering [14], WGCNA [43]) use distances in lieu of raw data to partition or 44 agglomerate observations into groups.”
• SPLINE-BACKFITTED KERNEL SMOOTHING OF ADDITIVE COEFFICIENT MODEL (2010, unknown): “Kernel procedures for the additive model, such as in Yang, Härdle, and Nielsen (1999), Sperlich, Tjøstheim, and Yang (2002), Yang, Sperlich, and Härdle (2003), Rodrı́guez-Póo, Sperlich, and Vieu (2003), and Hengartner and Sperlich (2005), satisfy criterion (iii) and partly (ii) as they are…”
• Estimation and Inference for Generalized Geoadditive Models (2020, unknown): “…for instance, the spline method of Stone (1986), Xue and Liang (2010), and Wang et al. (2011); the kernel method of Yu, Park, and Mammen (2008), Yang, Sperlich, and Härdle (2003), and Liang et al. (2008); and the two-stage methods of Horowitz and Mammen (2004), Wang and Yang (2007), and Liu,…”