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 539 articles , 1 standard article )

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  1. Ayyıldız, Ezgi; Purutçuoğlu, Vilda; Weber, Gerhard Wilhelm: Loop-based conic multivariate adaptive regression splines is a novel method for advanced construction of complex biological networks (2018)
  2. Chandra, Hukum; Salvati, Nicola; Chambers, Ray: Small area estimation under a spatially non-linear model (2018)
  3. Chatla, Suneel Babu; Shmueli, Galit: Efficient estimation of COM-Poisson regression and a generalized additive model (2018)
  4. Djeundje, Viani Biatat; Crook, Jonathan: Incorporating heterogeneity and macroeconomic variables into multi-state delinquency models for credit cards (2018)
  5. Gao, Guangyuan; Meng, Shengwang: Stochastic claims reserving via a Bayesian spline model with random loss ratio effects (2018)
  6. Guo, Jia; Zhou, Bu; Zhang, Jin-Ting: Testing the equality of several covariance functions for functional data: a supremum-norm based test (2018)
  7. Kim, Andy S. I.; Wand, Matt P.: On expectation propagation for generalised, linear and mixed models (2018)
  8. Li, Jianbo; Lian, Heng; Jiang, Xuejun; Song, Xinyuan: Estimation and testing for time-varying quantile single-index models with longitudinal data (2018)
  9. Liu, Jingyuan; Lou, Lejia; Li, Runze: Variable selection for partially linear models via partial correlation (2018)
  10. Li, Xinmin; Su, Haiyan; Liang, Hua: Fiducial generalized $p$-values for testing zero-variance components in linear mixed-effects models (2018)
  11. Li, Yu-Ning; Zhang, Yi: Estimation of heteroscedasticity by local composite quantile regression and matrix decomposition (2018)
  12. Randolph, Timothy W.; Zhao, Sen; Copeland, Wade; Hullar, Meredith; Shojaie, Ali: Kernel-penalized regression for analysis of microbiome data (2018)
  13. Schellhase, Christian; Spanhel, Fabian: Estimating non-simplified vine copulas using penalized splines (2018)
  14. Sun, Peng; Kim, Inyoung; Lee, Ki-Ahm: Dual-semiparametric regression using weighted Dirichlet process mixture (2018)
  15. Tang, Niansheng; Wu, Ying; Chen, Dan: Semiparametric Bayesian analysis of transformation linear mixed models (2018)
  16. Wojtyś, Małgorzata; Marra, Giampiero; Radice, Rosalba: Copula based generalized additive models for location, scale and shape with non-random sample selection (2018)
  17. Wyszynski, Karol; Marra, Giampiero: Sample selection models for count data in R (2018)
  18. Xiao, Luo; Li, Cai; Checkley, William; Crainiceanu, Ciprian: Fast covariance estimation for sparse functional data (2018)
  19. Dlugosz, Stephan; Mammen, Enno; Wilke, Ralf A.: Generalized partially linear regression with misclassified data and an application to labour market transitions (2017)
  20. Donat, Francesco; Marra, Giampiero: Semi-parametric bivariate polychotomous ordinal regression (2017)

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