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 399 articles , 1 standard article )

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  1. Donat, Francesco; Marra, Giampiero: Semi-parametric bivariate polychotomous ordinal regression (2017)
  2. Pande, Amol; Li, Liang; Rajeswaran, Jeevanantham; Ehrlinger, John; Kogalur, Udaya B.; Blackstone, Eugene H.; Ishwaran, Hemant: Boosted multivariate trees for longitudinal data (2017)
  3. Schulze Waltrup, Linda; Kauermann, Göran: Smooth expectiles for panel data using penalized splines (2017)
  4. Wood, Simon N.: Generalized additive models. An introduction with R. (2017)
  5. Yu, Yan; Wu, Chaojiang; Zhang, Yuankun: Penalised spline estimation for generalised partially linear single-index models (2017)
  6. Boojari, Hossein; Khaledi, Majid Jafari; Rivaz, Firoozeh: A non-homogeneous skew-gaussian Bayesian spatial model (2016)
  7. Bruno, Francesca; Greco, Fedele; Ventrucci, Massimo: Non-parametric regression on compositional covariates using Bayesian P-splines (2016)
  8. Gagnon, Jacob; Liang, Hua; Liu, Anna: Spherical radial approximation for nested mixed effects models (2016)
  9. Gao, Yan; Zhang, Xinyu; Wang, Shouyang; Zou, Guohua: Model averaging based on leave-subject-out cross-validation (2016)
  10. Heinzl, Felix; Tutz, Gerhard: Additive mixed models with approximate Dirichlet process mixtures: the EM approach (2016)
  11. Helwig, Nathaniel E.: Efficient estimation of variance components in nonparametric mixed-effects models with large samples (2016)
  12. Jamshidi, Arta A.; Powell, Warren B.: A recursive local polynomial approximation method using Dirichlet clouds and radial basis functions (2016)
  13. Jansen, Maarten: Non-equispaced B-spline wavelets (2016)
  14. Li, Degui; Li, Runze: Local composite quantile regression smoothing for harris recurrent Markov processes (2016)
  15. Li, Linyuan: Nonparametric regression on random fields with random design using wavelet method (2016)
  16. Osorio, Felipe: Influence diagnostics for robust P-splines using scale mixture of normal distributions (2016)
  17. Radice, Rosalba; Marra, Giampiero; Wojtyś, Małgorzata: Copula regression spline models for binary outcomes (2016)
  18. Sánchez-Borrego, I.R.; Soto, J.I.; Rueda, M.; Santos Betancor, I.: Nonparametric estimation to reconstruct the deformation history of an active fold in the Caspian Basin (2016)
  19. Sweeney, Elizabeth; Crainiceanu, Ciprian; Gertheiss, Jan: Testing differentially expressed genes in dose-response studies and with ordinal phenotypes (2016)
  20. Velasco-Cruz, Ciro; Contreras-Cruz, Luis Fernando; Smith, Eric P.; Rodríguez, José E.: A varying coefficients model for estimating finite population totals: a hierarchical Bayesian approach (2016)

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