Bayesian Smoothing and Regression Splines for Measurement Error Problems

By Scott M. Berry, Raymond J. Carroll, David Ruppert. 2002

219 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1ea9bbf2b728d61b34c851bde54e2af7fbadf86c

How researchers see this paper

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

In Statistics and Probability

Citations in the corpus

Computational Statistics and Data Analysis (unknown): “Berry et al. (2002) devised an elegant hierarchical Bayes approach to the simplest version of the problem and described Markov chain Monte Carlo (MCMC) based inference.”
Nonparametric Regression Estimation in the Heteroscedastic Errors-in-Variables Problem (2007, background): “…the nonparametric case, include Fan and Masry 1992; Cook and Stefanski 1994; Stefanski and Cook 1995; Carroll, Maca, and Ruppert 1999; Taupin 2001; Berry, Carroll, and Ruppert 2002; Carroll and Hall 2004; and Liang and Wang 2005; see also Carroll, Ruppert, Stefanski, and Crainiceanu 2006 for an…”
On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference (2015, methodology): “The function m(x) and the five evaluation points are plotted in Figure 1; this function was previously used by Berry et al. (2002) and Hall and Horowitz (2013). The supplement gives results for other models, bandwidth selectors and their simulation distributions, alternative variance estimators, and”
Bayesian Analysis for Penalized Spline Regression Using WinBUGS (2005, background): “In this paper we focus on semiparametric regression models using penalized splines (Ruppert et al. 2003), but the methodology can be extended to other penalized likelihood models.”
Penalized Wavelets: Embedding Wavelets into Semiparametric Regression (2011, background): “Elements of penalized splines Penalized splines are the building blocks of semiparametric regression models — a class of models that includes generalized additive models, generalized additive mixed models, varying coefficient models, geoadditive models, subject-specific curve models, among others (e”
Nonlinear and Nonparametric Regression and Instrumental Variables (2004, methodology): “Berry et al. (2002) found that P-splines and smoothing splines, which have a knot at every value of the covariate, generally give very similar answers, so one can use a large number of knots even if it is not necessary to do so. Powell’s (1981) results on the approximation properties of splines help”
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, Carroll”
Penalized Spline Estimation for Partially Linear Single-Index Models (2002, methodology): “In addition, P-splines can be viewed as mixed models or as hierarchical Bayesian models (see Brumback et al. 1999; Berry et al. 2002; Ruppert et al. 2002).”
Local polynomial regression and simulation–extrapolation (2004, methodology): “It is worth noting that the best performing estimators of Carroll et al. (1999) and Berry et al. (2002) are ‘structural’: they make strong assumptions about the distribution of {Xi}n1 (the distribution of the unobserved covariates, fx), and SIMEX is a ‘functional’ estimator that does not require…”