SemiPar

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 452 articles , 1 standard article )

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  1. Donat, Francesco; Marra, Giampiero: Semi-parametric bivariate polychotomous ordinal regression (2017)
  2. Fisher, Leigh; Wakefield, Jon; Bauer, Cici; Self, Steve: Time series modeling of pathogen-specific disease probabilities with subsampled data (2017)
  3. Goldman, Matt; Kaplan, David M.: Fractional order statistic approximation for nonparametric conditional quantile inference (2017)
  4. Holland, Ashley D.: Penalized spline estimation in the partially linear model (2017)
  5. Liao, Xiyue; Meyer, Mary C.: Change-point estimation using shape-restricted regression splines (2017)
  6. Mamouridis, Valeria; Klein, Nadja; Kneib, Thomas; Cadarso Suarez, Carmen; Maynou, Francesc: Structured additive distributional regression for analysing landings per unit effort in fisheries research (2017)
  7. Mhalla, Linda; Chavez-Demoulin, Valérie; Naveau, Philippe: Non-linear models for extremal dependence (2017)
  8. Montanari, Giorgio E.: Mixed model regression estimation of a spatial total in the continuous plane paradigm (2017)
  9. Pande, Amol; Li, Liang; Rajeswaran, Jeevanantham; Ehrlinger, John; Kogalur, Udaya B.; Blackstone, Eugene H.; Ishwaran, Hemant: Boosted multivariate trees for longitudinal data (2017)
  10. Sánchez-González, Mariola; Durbán, María; Lee, Dae-Jin; Cañellas, Isabel; Sixto, Hortensia: Smooth additive mixed models for predicting aboveground biomass (2017)
  11. Schulze Waltrup, Linda; Kauermann, Göran: Smooth expectiles for panel data using penalized splines (2017)
  12. Wan, Wen; Pei, Xin-Yan; Grant, Steven; Birch, Jeffrey B.; Felthousen, Jessica; Dai, Yun; Fang, Hong-Bin; Tan, Ming; Sun, Shumei: Nonlinear response surface in the study of interaction analysis of three combination drugs (2017)
  13. Wood, Simon N.: Generalized additive models. An introduction with R. (2017)
  14. Yu, Yan; Wu, Chaojiang; Zhang, Yuankun: Penalised spline estimation for generalised partially linear single-index models (2017)
  15. Zhang, Yuankun; Lian, Heng; Yu, Yan: Estimation and variable selection for quantile partially linear single-index models (2017)
  16. Arcuti, Simona; Pollice, Alessio; Ribecco, Nunziata; D’Onghia, Gianfranco: Bayesian spatiotemporal analysis of zero-inflated biological population density data by a delta-normal spatiotemporal additive model (2016)
  17. Bocci, Chiara; Petrucci, Alessandra; Rocco, Emilia: A two-part geoadditive small area model for geographical domain estimation (2016)
  18. Boojari, Hossein; Khaledi, Majid Jafari; Rivaz, Firoozeh: A non-homogeneous skew-gaussian Bayesian spatial model (2016)
  19. Bruno, Francesca; Greco, Fedele; Ventrucci, Massimo: Non-parametric regression on compositional covariates using Bayesian P-splines (2016)
  20. Gagnon, Jacob; Liang, Hua; Liu, Anna: Spherical radial approximation for nested mixed effects models (2016)

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