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.


References in zbMATH (referenced in 642 articles , 1 standard article )

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  1. Qin, Li; Guo, Wensheng: Functional mixed-effects model for periodic data (2006)
  2. Qu, Annie; Li, Runze: Quadratic inference functions for varying-coefficient models with longitudinal data (2006)
  3. Raach, Alexander Wolf: A Bayesian semiparametric latent variable model for binary, ordinal and continuous response. (2006)
  4. Sergeant, Jamie C.; Firth, David: Relative index of inequality: definition, estimation, and inference (2006)
  5. Sima, Diana M.; Van Huffel, Sabine: Regularized semiparametric model identification with application to nuclear magnetic resonance signal quantification with unknown macromolecular base-line (2006)
  6. Skaug, Hans J.; Fournier, David A.: Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models (2006)
  7. Wood, Simon N.: Generalized additive models. An introduction with R. (2006)
  8. Wood, Simon N.: Low-rank scale-invariant tensor product smooths for generalized additive mixed models (2006)
  9. Wood, Simon N.: On confidence intervals for generalized additive models based on penalized regression splines (2006)
  10. Yao, Fang; Lee, Thomas C. M.: Penalized spline models for functional principal component analysis (2006)
  11. Zhao, Y.; Staudenmayer, J.; Coull, B. A.; Wand, M. P.: General design Bayesian generalized linear mixed models (2006)
  12. Breidt, F. J.; Claeskens, G.; Opsomer, J. D.: Model-assisted estimation for complex surveys using penalised splines (2005)
  13. Brezger, Andreas: Bayesian P-splines in structured additive regression models. (2005)
  14. Crainiceanu, Ciprian; Ruppert, David; Claeskens, Gerda; Wand, M. P.: Exact likelihood ratio tests for penalised splines (2005)
  15. Ganguli, Bhaswati; Staudenmayer, John; Wand, M. P.: Additive models with predictors subject to measurement error (2005)
  16. Harezlak, Jaroslaw; Ryan, Louise M.; Giedd, Jay N.; Lange, Nicholas: Individual and population penalized regression splines for accelerated longitudinal designs (2005)
  17. Müller, Hans-Georg: Functional modelling and classification of longitudinal data (2005)
  18. Samworth, Richard; Poore, Heather: Understanding past ocean circulations: a nonparametric regression case study (2005)
  19. Song, Peter X.-K.; Fan, Yanqin; Kalbfleisch, John D.: Maximization by parts in likelihood inference (2005)
  20. Wang, Lan; Zhou, Xiaohua: A fully nonparametric diagnostic test for homogeneity of variances (2005)

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