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 640 articles , 1 standard article )

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  1. Wu, Jingwei; Peng, Hanxiang; Tu, Wanzhu: Large-sample estimation and inference in multivariate single-index models (2019)
  2. Xiao, Luo: Asymptotics of bivariate penalised splines (2019)
  3. Yoshida, Takuma: Two stage smoothing in additive models with missing covariates (2019)
  4. Akdeniz, Esra; Akdeniz, Fikri; Roozbeh, Mahdi: A new difference-based weighted mixed Liu estimator in partially linear models (2018)
  5. Aydin, Dursun; Yilmaz, Ersin: Modified estimators in semiparametric regression models with right-censored data (2018)
  6. 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)
  7. Calonico, Sebastian; Cattaneo, Matias D.; Farrell, Max H.: On the effect of bias estimation on coverage accuracy in nonparametric inference (2018)
  8. Chandra, Hukum; Salvati, Nicola; Chambers, Ray: Small area estimation under a spatially non-linear model (2018)
  9. Chatla, Suneel Babu; Shmueli, Galit: Efficient estimation of COM-Poisson regression and a generalized additive model (2018)
  10. Clairon, Quentin; Brunel, Nicolas J.-B.: Optimal control and additive perturbations help in estimating ill-posed and uncertain dynamical systems (2018)
  11. Djeundje, Viani Biatat; Crook, Jonathan: Incorporating heterogeneity and macroeconomic variables into multi-state delinquency models for credit cards (2018)
  12. Doss, Hani; Park, Yeonhee: An MCMC approach to empirical Bayes inference and Bayesian sensitivity analysis via empirical processes (2018)
  13. Gao, Guangyuan; Meng, Shengwang: Stochastic claims reserving via a Bayesian spline model with random loss ratio effects (2018)
  14. Guo, Jia; Zhou, Bu; Zhang, Jin-Ting: Testing the equality of several covariance functions for functional data: a supremum-norm based test (2018)
  15. Harezlak, Jaroslaw; Ruppert, David; Wand, Matt P.: Semiparametric regression with R (2018)
  16. Kim, Andy S. I.; Wand, Matt P.: On expectation propagation for generalised, linear and mixed models (2018)
  17. Li, Jianbo; Lian, Heng; Jiang, Xuejun; Song, Xinyuan: Estimation and testing for time-varying quantile single-index models with longitudinal data (2018)
  18. Li, Liang; Wu, Chih-Hsien; Ning, Jing; Huang, Xuelin; Shih, Ya-Chen Tina; Shen, Yu: Semiparametric estimation of longitudinal medical cost trajectory (2018)
  19. Liu, Jingyuan; Lou, Lejia; Li, Runze: Variable selection for partially linear models via partial correlation (2018)
  20. Li, Xinmin; Su, Haiyan; Liang, Hua: Fiducial generalized (p)-values for testing zero-variance components in linear mixed-effects models (2018)

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