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.

References in zbMATH (referenced in 640 articles , 1 standard article )

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  1. Crainiceanu, Ciprian M.; Ruppert, David: Restricted likelihood ratio tests in nonparametric longitudinal models. (2004)
  2. Crainiceanu, Ciprian M.; Ruppert, David: Likelihood ratio tests for goodness-of-fit of a nonlinear regression model (2004)
  3. Crainiceanu, Ciprian M.; Ruppert, David: Likelihood ratio tests in linear mixed models with one variance component (2004)
  4. Denuit, Michel; Lang, Stefan: Non-life rate-making with Bayesian GAMs (2004)
  5. Fahrmeir, Ludwig; Kneib, Thomas; Lang, Stefan: Penalized structured additive regression for space-time data: a Bayesian perspective. (2004)
  6. Genovese, Christopher R.; Miller, Christopher J.; Michol, Robert C.; Arjunwadkar, Mihir; Wasserman, Larry: Nonparametric inference for the cosmic microwave background (2004)
  7. Hu, Zonghui; Wang, Naisyin; Carroll, Raymond J.: Profile-kernel versus backfitting in the partially linear models for longitudinal/clustered data. (2004)
  8. Jarrow, Robert; Ruppert, David; Yu, Yan: Estimating the interest rate term structure of corporate debt with a semiparametric penalized spline model. (2004)
  9. Ren, Haobo; Zhou, Xiao-Hua; Liang, Hua: A flexible method for estimating the ROC curve (2004)
  10. Schucany, William R.: Kernel smoothers: an overview of curve estimators for the first graduate course in nonparametric statistics (2004)
  11. Tutz, Gerhard: Generalized semiparametrically structured mixed models (2004)
  12. Wager, C. G.; Coull, B. A.; Lange, N.: Modelling spatial intensity for replicated inhomogeneous point patterns in brain imaging (2004)
  13. Kim, Inyoung; Cohen, Noah D.; Carroll, Raymond J.: Semiparametric regression splines in matched case-control studies (2003)
  14. Ruppert, David; Wand, M. P.; Carroll, R. J.: Semiparametric regression. (2003)
  15. Wand, M. P.: Smoothing and mixed models (2003)
  16. Racine, Jeff: Parallel distributed kernel estimation (2002)
  17. Yu, Yan; Ruppert, David: Penalized spline estimation for partially linear single-index models. (2002)
  18. Cheung, Ying-Kuen; Fine, Jason P.: Likelihood estimation after nonparametric transformation (2001)
  19. Ye, Jianming; Duan, Naihua: Nonparametric (n^-1/2)-consistent estimation for the general transformation models (1997)
  20. Wang, Naisyin; Ruppert, David: Estimation of regression parameters in a semiparametric transformation model (1996)

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