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 517 articles , 1 standard article )

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  1. Gao, Guangyuan; Meng, Shengwang: Stochastic claims reserving via a Bayesian spline model with random loss ratio effects (2018)
  2. Kim, Andy S.I.; Wand, Matt P.: On expectation propagation for generalised, linear and mixed models (2018)
  3. Li, Yu-Ning; Zhang, Yi: Estimation of heteroscedasticity by local composite quantile regression and matrix decomposition (2018)
  4. Schellhase, Christian; Spanhel, Fabian: Estimating non-simplified vine copulas using penalized splines (2018)
  5. Tang, Niansheng; Wu, Ying; Chen, Dan: Semiparametric Bayesian analysis of transformation linear mixed models (2018)
  6. Xiao, Luo; Li, Cai; Checkley, William; Crainiceanu, Ciprian: Fast covariance estimation for sparse functional data (2018)
  7. Donat, Francesco; Marra, Giampiero: Semi-parametric bivariate polychotomous ordinal regression (2017)
  8. Ferrara, Giancarlo; Vidoli, Francesco: Semiparametric stochastic frontier models: a generalized additive model approach (2017)
  9. Fisher, Leigh; Wakefield, Jon; Bauer, Cici; Self, Steve: Time series modeling of pathogen-specific disease probabilities with subsampled data (2017)
  10. Goldman, Matt; Kaplan, David M.: Fractional order statistic approximation for nonparametric conditional quantile inference (2017)
  11. Holland, Ashley D.: Penalized spline estimation in the partially linear model (2017)
  12. Ivanescu, Andrada E.: Adaptive inference for the bivariate mean function in functional data (2017)
  13. Liao, Xiyue; Meyer, Mary C.: Change-point estimation using shape-restricted regression splines (2017)
  14. Liu, Chong; Ray, Surajit; Hooker, Giles: Functional principal component analysis of spatially correlated data (2017)
  15. 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)
  16. Marra, Giampiero; Radice, Rosalba: A joint regression modeling framework for analyzing bivariate binary data in $\mathsfR$ (2017)
  17. Mhalla, Linda; Chavez-Demoulin, Valérie; Naveau, Philippe: Non-linear models for extremal dependence (2017)
  18. Montanari, Giorgio E.: Mixed model regression estimation of a spatial total in the continuous plane paradigm (2017)
  19. Pande, Amol; Li, Liang; Rajeswaran, Jeevanantham; Ehrlinger, John; Kogalur, Udaya B.; Blackstone, Eugene H.; Ishwaran, Hemant: Boosted multivariate trees for longitudinal data (2017)
  20. Sánchez-González, Mariola; Durbán, María; Lee, Dae-Jin; Cañellas, Isabel; Sixto, Hortensia: Smooth additive mixed models for predicting aboveground biomass (2017)

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