Smoothing and mixed models

By Matt P. Wand. Computational statistics (Zeitschrift), 2003

345 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/50560fcddbdb39e1b70b034e6cf97157996cc4d3

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

A Note on Penalized Spline Smoothing With Correlated Errors (2007, methodology): “In fact, even smallest misspecifications of the correlation structure can result in serious over (or under) fitting as demonstrated in Opsomer, Wang & Yang (2001). This exhibits an undesirable sensitivity of MSEbased smoothing parameter selectors.”
Smoothing and forecasting mortality rates (2004, methodology): “The review article by Wand (2003) adopts a mixed model approach and provides a useful bibliography.”
Nonparametric small area estimation using penalized spline regression (2005, neutral): “Parise et al. (2001), Coull, Ruppert, and Wand (2001), Coull, Schwartz, and Wand (2001) and Liang (2003) all provide examples of using penalized splines in the construction of mixed effect regression models for the analysis of data containing random effects.”
Non‐parametric small area estimation using penalized spline regression (2008, methodology): “In the current paper, we exploit the close connection between P-splines and linear mixed models (see Wand (2003)) to show how to incorporate a non-parametric mean function specification into existing small area estimation approaches.”
Twenty years of P-splines (2015, background): “Proponents of TPF have avoided this issue by using radial basis functions (Ruppert et al., 2003; Kammann and Wand, 2003).”
General design Bayesian generalized linear mixed models (2006, methodology): “|r/p|)(l + \r/p\) corresponds to Mat?rn low rank kriging with range p > 0 and smoothness parame ter set to 3/2 (as defined in Stein, 1999; Kammann and Wand, 2003).”
Bayesian Analysis for Penalized Spline Regression Using WinBUGS (2005, methodology): “Given this equivalence, statistical software developed for mixed models, such as S-plus (function lme) or SAS (PROC MIXED and the GLIMMIX macro) can be used for smoothing (Wand [29], Ngo and Wand [20]).”
Structured Additive Regression for Categorical Space–Time Data: A Mixed Model Approach (2006, methodology): “…Zhang (2004) used smoothing splines and random effects to model longitudinal data with responses from a univariate exponential family, Kammann and Wand (2003) introduced geoadditive models for Gaussian responses based on Gaussian random fields (GRFs) and P-splines, and Fahrmeir, Kneib, and Lang…”
Smooth Transition from Fixed Effects to Mixed Effects Models in Multi-level regression Models (2008, background): “More recently now, they have entered the nonparametric world, see Ruppert, Wand and Carroll (2003), Wand (2003), Hamilton (2001), Opsomer, Claeskens, Ranalli, Kauermann and Breidt (2005), Tutz (2001), and Tutz and Reithinger (2007).”
Some asymptotic results on generalized penalized spline smoothing (2009, background): “prehending the penalty imposed on the spline coefficients as aGaussian prior, see Wand (2003) .”
Correcting for spatial heterogeneity in plant breeding experiments with P-splines (2018, methodology): “We propose the use of tensor product P-splines (Eilers and Marx, 2003) to explicitly model both sources of spatial dependence.”
Theory for penalised spline regression (2005, result): “This explains the difference between our results and those of Wand (1999). Formula (35) can potentially be used to choose A adaptively via a plug-in rule.”
P-spline ANOVA-type interaction models for spatio-temporal smoothing (2011, methodology): “…set of models (random effects models, correlated data or longitudinal studies, survival analysis), and the use of the methodology already developed for mixed models for the estimation and inference (see Brumback and Rice, 1998; Verbyla et al., 1999; Wand, 2003; Welham et al., 2007, among others).”