Spline Adaptation in Extended Linear Models (with comments and a rejoinder by the authors

By Mark H. Hansen, Charles L. Kooperberg. 2002

85 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2788bb6e3136e3d4b3df92095fd0d1215c373381

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Citations in the corpus

Monotonic Regression Based on Bayesian P–Splines (2008, methodology): “…approaches make use of adaptive knot selection (e.g., Smith and Kohn 1996; Denison, Mallick and Smith 1998; Biller 2000; Di Matteo, Genovese and Kass 2001; Biller and Fahrmeir 2001; Hansen and Kooperberg 2002) or smoothness priors (Hastie and Tibshirani 2000; Fahrmeir and Lang 2001a,b).”
Inference in Semiparametric Dynamic Models for Binary Longitudinal Data (2006, methodology): “…on the basis of their Bayes factor, is a large-dimensional integral, previous work (e.g., DiMatteo, Genovese, and Kass 2001; Wood, Kohn, Shively, and Jiang 2002; Hansen and Kooperberg 2002) has relied on measures such as the Akaike information criterion (AIC) and Bayes information criterion (BIC).”
Generalized Nonlinear Modeling With Multivariate Free-Knot Regression Splines (2003, background): “Gao et al. (2001) suggested Bayesian alternatives, and Hansen and Kooperberg (2002) compared Bayesian and non-Bayesian approaches.”
Functional Coefficient Regression Models for Non‐linear Time Series: A Polynomial Spline Approach (2004, background): “See Stone et al. (1997) and Hansen & Kooperberg (2002) for recent reviews of the free-knot methodology.”
Bayesian P-Splines (2004, background): “Hansen and Kooperberg (2002) discussed adaptive knot selection for the very broad class of extended linear models.”
Multimodel Inference (2004, result): “…that has evaluated, or even considered, model averaged inferences compared to the best model strategy that model averaging is superior (e.g., Buckland et al. 1997; Hoeting et al. 1999; Wasserman 2000; Breiman 2001; Burnham and Anderson 2002; Hansen and Kooperberg 2002; Lukacs et al. in review).”
A Quantitative Study of Gene Regulation Involved in the Immune Response of Anopheline Mosquitoes (2006, background): “(For general discussions of basis function representations and the so-called extended linear model, see, e.g., Vidakovic 1999; Schimek 2000; Denison et al. 2002; Hansen and Kooperberg 2002.)”
PENALIZED STRUCTURED ADDITIVE REGRESSION FOR SPACE-TIME DATA: A BAYESIAN PERSPECTIVE (2004, background): “Bayesian variants can be found in Denison et al. (1998), Biller (2000), Di Matteo et al. (2001), Biller and Fahrmeir (2001), and Hansen and Kooperberg (2002).”
Generalized structured additive regression based on Bayesian P-splines (2006, methodology): “…based on regression splines with adaptive knot selection (e.g. Smith and Kohn, 1996; Denison, Mallick and Smith, 1998; Biller, 2000; Di Matteo, Genovese and Kass, 2001; Biller and Fahrmeir, 2001; Hansen and Kooperberg, 2002), or on smoothness priors (Hastie and Tibshirani (2000) and own work).”
Statistical issues in the analysis of neuronal data. (2005, methodology): “However, nonparametric approaches using BARS or related methods (cf. Hansen and Kooperberg 2002; Loader 1999) are often easier to implement, remain accurate when the parametric form is incorrect, and suffer relatively little loss of efficiency even when the parametric form holds.”
Bayesian Analysis for Penalized Spline Regression Using WinBUGS (2005, methodology): “…Wahba 1990), series-based smoothers (Ogden 1996; Tarter and Lock 1993), kernel methods (Fan and Gijbels 1996; Wand and Jones 1995), regression splines (Friedman 1991; Hansen and Kooperberg 2002; Hastie and Tibshirani 1990), penalized splines (Eilers and Marx 1996; Ruppert, Wand, and Carroll 2003).”
Penalized triograms: total variation regularization for bivariate smoothing (2004, unknown): “This L2-variant of the penalized triogram has been explored further by Hansen and Kooperberg (2002).”