SemiPar
R package SemiPar: Semiparametic Regression. The primary aim of this book is to guide researchers needing to flexibly incorporate nonlinear relations into their regression analyses. Almost all existing regression texts treat either parametric or nonparametric regression exclusively. In this book the authors argue that nonparametric regression can be viewed as a relatively simple extension of parametric regression and treat the two together. They refer to this combination as semiparametric regression. The approach to semiparametric regression is based on penalized regression splines and mixed models. Every model in this book is a special case of the linear mixed model or its generalized counterpart. This book is very much problem-driven. Examples from their collaborative research have driven the selection of material and emphases and are used throughout the book. The book is suitable for several audiences. One audience consists of students or working scientists with only a moderate background in regression, though familiarity with matrix and linear algebra is assumed. Another audience that they are aiming at consists of statistically oriented scientists who have a good working knowledge of linear models and the desire to begin using more flexible semiparametric models. There is enough new material to be of interest even to experts on smoothing, and they are a third possible audience. This book consists of 19 chapters and 3 appendixes.
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References in zbMATH (referenced in 705 articles , 1 standard article )
Showing results 581 to 600 of 705.
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- Zhou, Lan; Huang, Jianhua Z.; Carroll, Raymond J.: Joint modelling of paired sparse functional data using principal components (2008)
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- Breidt, F. Jay; Hsu, Nan-Jung; Ogle, Stephen: Semiparametric mixed models for increment-averaged data with application to carbon sequestration in agricultural soils (2007)
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- Flaschel, Peter; Kauermann, Göran; Semmler, Willi: Testing wage and price Phillips curves for the United States (2007)
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- Haldermans, Philippe; Shkedy, Ziv; van Sanden, Suzy; Burzykowski, Tomasz; Aerts, Marc: Using linear mixed models for normalization of CDNA microarrays (2007)
- Harezlak, Jaroslaw; Coull, Brent A.; Laird, Nan M.; Magari, Shannon R.; Christiani, David C.: Penalized solutions to functional regression problems (2007)
- Hazelton, Martin L.: Bias reduction in kernel binary regression (2007)
- Helton, J. C.; Johnson, J. D.; Oberkampf, W. L.; Storlie, C. B.: A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory (2007)
- Hyndman, Rob J.; Ullah, Md. Shahid: Robust forecasting of mortality and fertility rates: a functional data approach (2007)
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- Jiang, Jiancheng; Zhou, Haibo; Jiang, Xuejun; Peng, Jianan: Generalized likelihood ratio tests for the structure of semiparametric additive models (2007)
- Jullion, Astrid; Lambert, Philippe: Robust specification of the roughness penalty prior distribution in spatially adaptive Bayesian P-splines models (2007)