Semiparametric Regression: Preface

By David Ruppert, Matt P. Wand, Raymond J. Carroll. 2003

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Modeling Spatiotemporal Forest Health Monitoring Data (2009, methodology): “Generalized additive mixed models (GAMMs) (Lin and Zhang, 1999; Kamman and Wand, 2003; Ruppert et al., 2003) have been used to model spatio-temporal data and provide the general framework to fulfill the model requirements.”
Smoothing: Local Regression Techniques (2012, background): “See Chapter 3 of Ruppert et al. (2003) for a more detailed discussion of regression splines and basis functions.”
On the asymptotics of penalized spline smoothing (2011, methodology): “[22], a penalized spline in one basis will be algebraically identical to a penalized spline in a second basis, if the two bases span the same vector space of functions and if they use identical penalties.”
Likelihood Ratio Tests for Dependent Data with Applications to Longitudinal and Functional Data Analysis (2014, methodology): “Here, x p C D max.0; x/p , 1; : : : ; K are knots placed at equally spaced quantiles, andK is assumed to be large enough to ensure the desired flexibility (Ruppert, 2002; Ruppert et al., 2003).”
Back to the Future: Modeling Time Dependence in Binary Data (2010, background): “See Ruppert, Wand, and Carroll (2003) and Keele (2008) for details on automated smoothing methods.”
High dimensional structured additive regression models: Bayesian regularization, smoothing and predictive performance (2011, methodology): “Previous research has mostly concentrated either on penalization approaches for semiparametric smoothing (Eilers and Marx, 1996; Ruppert et al., 2003; Fahrmeir et al., 2004; Wood, 2006) or on regularizing high dimensional but exclusively linear predictors.”
Model-assisted estimation for complex surveys using penalised splines (2005, methodology): “For data-driven selection of the value of α in the regression context, Ruppert et al. (2003) recommend treating the estimator (5) as resulting from a linear mixed model specification, and using Restricted Maximum Likelihood (REML) (Patterson and Thompson, 1971; Searle et al. 1992) to estimate an…”
Propriety of posteriors in structured additive regression models: Theory and empirical evidence (2009, background): “…considerable attention throughout recent years and has been used to estimate semiparametric regression models in a variety of settings (compare Ruppert et al. (2003) for an overview, Fahrmeir et al. (2004) for results on exponential family STAR models, and Kneib & Fahrmeir (2007) for mixed…”
Theory and inference for a class of nonlinear models with application to time series of counts (2016, background): “Readers can refer to Davis, Dunsmuir, and Streett (2003) and Jung and Tremayne (2011) for a comprehensive treatment of the tools.”
Functional Modelling and Classification of Longitudinal Data * (2005, background): “…principal components requires some form of regularization (Rice & Silverman, 1991) which can be achieved with smoothing methods; smoothing splines, B-splines, P-splines (compare Ruppert et al., 2003), kernel smoothing and local weighted least squares have been considered for this purpose.”
Spatial smoothing and hot spot detection for CGH data using the fused lasso. (2008, methodology): “Microarrays, empirical Bayes methods, and false discovery rates.”