Bayesian varying-coefficient models using adaptive regression splines

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Generalized structured additive regression based on Bayesian P-splines (2006, methodology): “Bayesian approaches are currently either based on regression splines with 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 on smoothness priors (Hastie a”
PENALIZED STRUCTURED ADDITIVE REGRESSION FOR SPACE-TIME DATA: A BAYESIAN PERSPECTIVE (2004, background): “Bayesian variants can be found in Denison, Mallick and Smith (1998), Biller (2000), Di Matteo, Genovese and Kass (2001), Biller and Fahrmeir (2001) and Hansen and Kooperberg (2002).”
Bayesian measures of model complexity and fit (2002, methodology): “, 2001), spline models with Bernoulli responses (Biller and Fahrmeir, 2001), multistage models for treatment usage which combine to form a total DIC (Gelfand et al.”
Monotonic Regression Based on Bayesian P–Splines (2008, methodology): “…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 Lang 2001a,b).”