Partially Linear Models

By Wolfgang Karl Härdle, Hua Liang, Jiti Gao. 2000

722 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Artificial Intelligence, Statistics and Probability

How researchers see this paper

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

In Analytical Chemistry

1. Yingcun Xia
2. Jianqing Fan
3. Hua Liang
4. Guang Cheng
5. Lijian Yang
6. Qi Li
7. Wenyang Zhang
8. Wolfgang Karl Härdle

In Artificial Intelligence

1. Jianqing Fan
2. Hua Liang
3. R. Caruana
4. Chenlei Leng
5. Wenyang Zhang
6. Yin Lou
7. Byeong U. Park
8. Heng Lian

In Statistics and Probability

1. Jianqing Fan
2. Hua Liang
3. Chenlei Leng
4. Yingcun Xia
5. Byeong U. Park
6. Wenyang Zhang
7. Tao Hu
8. Heng Lian

Citations in the corpus

• Sparse Partially Linear Additive Models (2014, methodology): “hms that are applied to real data sets with half a million samples and over 45,000 features with excellent predictive performance. 1 Introduction Generalized partially linear additive models (GPLAMs) [1] provide an attractive middle ground between the simplicity of generalized linear models (GLMs) [”
• Estimation and Testing for Varying Coefficients in Additive Models With Marginal Integration (2006, methodology): “…include additive models (Chen and Tsay 1993a; Linton and Nielsen 1995; Masry and Tjøstheim 1995, 1997; Mammen, Linton, and Nielsen 1999; Sperlich, Tjøstheim, and Yang 2002), generalized additive models (Linton and Härdle 1996), partially linear models (Härdle, Liang, and Gao 2000), and the like.”
• Journal of the American Statistical Association Linear or Nonlinear? Automatic Structure Discovery for Partially Linear Models Linear or Nonlinear? Automatic Structure Discovery for Partially Linear Models (neutral): “A recent monograph by Hardle, Liang, and Gao (2000) gave an excellent overview on partially linear models, and a more comprehensive list of references can be found there.”
• Noname manuscript No. (will be inserted by the editor) A semiparametric Bernstein- von Mises theorem for Gaussian process priors (neutral): “the period estimation considered in [4]), the density estimation version of model (9), the class of partially linear models with Gaussian errors, see [14], to name a few.”
• Model-Free Feature Screening for Ultrahigh Dimensional Data (2011, background): “Note that many existing regression models for a continuous response can be written in the following form: (Härdle, Liang, and Gao 2000), the single-index model (Härdle, Hall, and Ichimura 1993), and the partially linear single-index model (Carroll et al. 1997).”
• Profile‐kernel versus backfitting in the partially linear models for longitudinal/clustered data (2004, methodology): “The bandwidth h was chosen by ‘leaving one subject out’ crossvalidation (Rice & Silverman, 1991; Härdle et al., 2000, § 2.1.3) using the profile-kernel method.”
• Efficient estimation of a semiparametric partially linear varying coefficient model (2005, methodology): “Li, Huang, Li and Fu (2002) use the nonparametric kernel method to estimate the semiparametric varying coefficient model (2) and apply the method to China’s nonmetal mineral manufacturing industry data; their results show that the semiparametric varying coefficient model (2) is more appropriate than”
• A Mallows-Type Model Averaging Estimator for the Varying-Coefficient Partially Linear Model (2018, background): “See Härdle, Liang, and Gao (2000) for a review.”