Bayesian mixture of splines for spatially adaptive nonparametric regression

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Local Spectral Analysis via a Bayesian Mixture of Smoothing Splines (2009, methodology): “To facilitate the computations we follow Wood, Jiang, and Tanner (2002) and write h ¼ ðhðn0Þ; . . . ; hðnn=2ÞÞ0 as a linear combination of basis functions so that h ¼ Xb, where the columns of the design matrix X are the Demmler-Reinsch basis functions evaluated at the Fourier frequencies and b is…”
Regression Density Estimation With Variational Methods and Stochastic Approximation (2012, background): “Wood, Jiang, and Tanner (2002) and Wood et al. (2008) considered models with flexible terms for the covariates for continuous and binary responses, respectively.”
Flexible modeling of conditional distributions using smooth mixtures of asymmetric student t densities (2010, unknown): “Villani et al. (2009) argue in favor of a complex-and-few modeling philosophy where enough flexibility is used within the mixture components, so that the number of components can be kept to a minimum; see also Wood et al. (2002).”
Nonstationary Covariance Functions for Gaussian Process Regression (2003, background): “The second is a real dataset of air temperature as a function of latitude and longitude [17] that allows assessment on a spatial dataset with distinct variable smoo thness.”