Nonparametric Regression and Generalized Linear Models: A roughness penalty approach

By Peter J. Green, Bernard Walter Silverman. 1993

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In Statistics and Probability

Citations in the corpus

Sparse Partially Linear Additive Models (2014, methodology): “GPLAMs are a popular tool for data analysis in multiple domains including economics (Engle et al., 1986; Green and Silverman, 1993) and biology (Lian et al.”
Sufficient Forecasting Using Factor Models (2015, methodology): “mploy one of the well-developed nonparametric regression techniques to estimate h() and make the forecast, including local polynomial modeling, regression splines, additive modeling, and many others (Green and Silverman, 1993; Fan and Gijbels, 1996; Li and Racine, 2007). We summarize the sucient for”
A review of spline function procedures in R (2019, methodology): “(3) to higher-dimensional x i (see [41], Section 4.15], and [11] for details).”
Nonconcave penalized likelihood with a diverging number of parameters (2004, methodology): “nparametric problems as large parametric problems and extend traditional variable selection techniques to select important terms. Smoothing splines can also be regarded as a large parametric problem [Green and Silverman (1994)]. To achieve the stability of the resulting estimate (e.g., smoothness), ”
Nonparametric Multiplicative Regression for Habitat Modeling (2011, unknown): “…of books and papers in the statistical literature on smoothing techniques and nonparametric regression (Bowman & Azzalini 1997, Eubank 1999, Fan & Gijbels 1996, Green & Silverman 1994, Hastie et al. 2001, Scott 1992, Wand and Jones 1995), these techniques have largely been ignored by ecologists.”
Statistics of Extremes (2004, background): “2 of Green and Silverman (1994), it can be shown that the maximization of (7.”
Statistical Methods with Varying Coefficient Models. (2008, background): “1997), partially linear models (Wahba 1984; Green and Silverman 1994), and their hybrids (Carroll et al.”