Smoothing with Mixed Model Software
Long Ngo,Matt P. Wand +1 more
TL;DR: This work illustrates that software for mixed model analysis can be used for smoothing for several smoothing models such as additive and varying coefficient models for both S-PLUS and SAS software.
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Abstract: Smoothing methods that use basis functions with penalization can be formulated as fits in a mixed model framework One of the major benefits is that software for mixed model analysis can be used for smoothing We illustrate this for several smoothing models such as additive and varying coefficient models for both S-PLUS and SAS software Code for each of the illustrations is available on the Internet
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Citations
Spatial Modelling Using a New Class of Nonstationary Covariance Functions.
TL;DR: A new class of nonstationary covariance functions for spatial modelling, which includes a non stationary version of the Matérn stationary covariance, in which the differentiability of the spatial surface is controlled by a parameter, freeing one from fixing the differentiable in advance.
Semiparametric regression during 2003–2007
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On semiparametric regression with O'Sullivan penalised splines
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TL;DR: In this paper, the use of O'Sullivan penalized splines in contemporary semiparametric regression, including mixed model and Bayesian formulations, is discussed. And exact expressions for the OSullivan penalty matrix are obtained.
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Bayesian Hierarchical Spatially Correlated Functional Data Analysis with Application to Colon Carcinogenesis
Veerabhadran Baladandayuthapani,Bani K. Mallick,Mee Young Hong,Mee Young Hong,Joanne R. Lupton,Nancy D. Turner,Raymond J. Carroll +6 more
TL;DR: New methods to analyze data from an experiment using rodent models to investigate the role of p27, an important cell‐cycle mediator, in early colon carcinogenesis are presented and suggest the existence of significant crypt signaling.
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