Bayesian curve-fitting with free-knot splines

By Ilaria Dimatteo, Christopher R. Genovese, Robert E. Kass. 2001

487 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computational Theory and Mathematics, Geometry and Topology

View Paper: https://www.semanticscholar.org/paper/4e5e0335247e077dc26496960cd172c205885151

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In Computational Mechanics

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Citations in the corpus

Free‐knot polynomial splines with confidence intervals (2003, background): “Some more recent, effective variations are in Smith and Kohn (1996), Denison et al. (1998), Lindstrom (1999), DiMatteo et al. (2001) and Zhou and Shen (2001).”
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) .”
Adaptive Free-Knot Splines (2003, methodology): “Works along this direction can be found in Donoho and Johnstone (1995), who proposed a wavelet shrinkage method; in Fan and Gijbels (1995), who employed adaptive local polynomials; in Lou and Wahba (1997), who used adaptive smoothingsplines; and in Denison, Mallick, and Smith (1996), who used regres”
Spatially Adaptive Bayesian Penalized Regression Splines (P-splines) (2005, methodology): “The BARS program was graciously provided by the authors of DiMatteo et al. (2001).”
Parameter estimation of ODE’s via nonparametric estimators (2007, background): “As suggested before, one can think of an extension of the celebrated GCV, but in our setting this problem of knots selection seems more naturally dealt by the free-knot splines [3, 18].”
Handbook of Markov Chain Monte Carlo: Hardcover: 619 pages Publisher: Chapman and Hall/CRC Press (first edition, May 2011) Language: English ISBN-10: 1420079417 (2012, methodology): “…3.1), reducing the sampler either to ﬁxed dimensions, for example on model space k ∈ K only, or to a lower-dimensional set of model and parameter Handbook of Markov Chain Monte Carlo space (Berger and Pericchi, 2001; DiMatteo et al., 2001; George and McCulloch, 1993; Tadesse et al., 2005).”
Nonparametric binary regression using a Gaussian process prior (2007, methodology): “[5] described a free-knot spline approach for likelihood based models, where”
Functional, but Not Anatomical, Separation of "what" and "when" in Prefrontal Cortex Gaussian-process Factor Analysis for Low-dimensional Single-trial Analysis of Neural Population Activity (unknown): “…to be similar on different trials) (Broome et al. 2006; Brown et al. 2005; Mazor and Laurent 2005; Stopfer et al. 2003) or by applying more advanced statistical methods for estimating firing-rate profiles from single spike trains (Cunningham et al. 2008b; DiMatteo et al. 2001; Ventura et al. 2005).”
Inference in Semiparametric Dynamic Models for Binary Longitudinal Data (2006, methodology): “…marginal likelihood, which is used to compare two models 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…”
Statistical issues in the analysis of neuronal data. (2005, background): “BARS stands for Bayesian adaptive regression splines (DiMatteo et al. 2001).”
Isotonic Smoothing Spline Regression (2008, background): “Bayesian monotone free-knot splines (Dimatteo, Genovese, and Kass 2001) are also currently studied for monotone regression.”