Adaptive Bayesian Regression Splines in Semiparametric Generalized Linear Models

By C. Biller. 2000

109 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/deb6cf40cd8880eec237367dab19f7cd65cbb8e2

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In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

WinBUGS for population ecologists: bayesian modeling using markov chain Monte Carlo methods (2009, methodology): “Note that an alternative to P-splines called adaptive splines (Biller 2000) is considered in the mark-recapture context by Bonner et al. (this volume).”
Monotonic Regression Based on Bayesian P–Splines (2008, methodology): “Bayesian nonparametric 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…”
PENALIZED STRUCTURED ADDITIVE REGRESSION FOR SPACE-TIME DATA: A BAYESIAN PERSPECTIVE (2004, background): “(1998), Biller (2000), Di Matteo et al. (2001), Biller and Fahrmeir (2001), and Hansen and Kooperberg (2002).”
Bayesian inference for generalized additive mixed models based on Markov random field priors (2001, methodology): “The more recent RJMCMC al-gorithm developed by Biller (1999) for adaptive regression splines in semiparametricGLMs seems to have better convergence properties, and an extension to GAMMSand VCMMs might be promising.”
Nonstationary Covariance Functions for Gaussian Process Regression (2003, methodology): “Assu ming independence between years [19], conditional on f(·) = logit(p(·)), the likelihood for a given calendar day,xi, is binomial with two trials and unknown probability of rain fall, p(xi).”
BAYESIAN ESTIMATION IN SINGLE-INDEX MODELS (2004, methodology): “Bayesian approaches have been successful with many other nonlinear regression models in recent years, e.g., additive semiparametric regression ( Biller (2000) ), semiparametric hazard function regression (McKeague and Tighiouart (2000)), nonparametric regression with measurement error (Berry, Carrol”
Generalized Nonlinear Modeling With Multivariate Free-Knot Regression Splines (2003, unknown): “Biller (2000) discussed knot selection for curve tting of non-Gaussiandata using B splines.”
Generalized structured additive regression based on Bayesian P-splines (2006, methodology): “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). Inference is based on Markov chain Monte Carlo inference techniques.”
Bayesian P-Splines (2004, methodology): “° AdaptiveBayesian regression splines by Biller (2000) as an example of a competing Bayesian approach.”
Bayesian varying-coefficient models using adaptive regression splines (2001, methodology): “In this paper we present an extension of the adaptive Bayesian regression spline approach for semi-parametric GLM’s in Biller (2000) to a Bayesian version of the varying coefficient models (1).”