Nonparametric and Semiparametric Models
By Wolfgang Karl Härdle. 2004
506 citations recorded by the source corpus.
Introduction.- Histogram.- Nonparametric Density Estimation.- Nonparametric Regression.- Semiparametric and Generalized Regression Models.- Single Index Models.- Generalized Partial Linear Models.- Additive Models and Marginal Effects.- Generalized Additive Models.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/d4ee83ed8de2faf0693ae7cef10b87b506ccb701
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
- Jan Peters
- Pradeep Ravikumar
- Han Liu
- Larry A. Wasserman
- Duy Nguyen-Tuong
- John D. Lafferty
- Sergey Levine
- Bernhard Schölkopf
In Statistics and Probability
- Larry A. Wasserman
- Han Liu
- Jan Peters
- Pradeep Ravikumar
- John D. Lafferty
- Mathias Drton
- Hua Liang
- Peter Buhlmann
Citations in the corpus
- Sparse additive models (2007, methodology): “These results are basically consistent with those obtained by other authors (Härdle et al., 2004).”
- A Brief Review of Domain Adaptation (2020, background): “However, indirect density estimation is usually ineffective and a very challenging task, as the importance is usually unknown in reality, especially when having high dimensional features [39].”
- Efficiency, subsidies and environmental adaptation of animal farming under CAP (2005, methodology): “For a more detailed introduction to non and semiparametric modeling see also Härdle et al. (2004). The basic idea is to construct an estimator that creates a smooth surface (or hyperplane), that is, in the one-dimensional case a smooth line, into the point cloud that presents its functional form.”