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 758 articles , 1 standard article )

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  1. Balasubramanian, Krishnakumar; Ghadimi, Saeed; Nguyen, Anthony: Stochastic multilevel composition optimization algorithms with level-independent convergence rates (2022)
  2. Bauer, Verena; Harhoff, Dietmar; Kauermann, Göran: A smooth dynamic network model for patent collaboration data (2022)
  3. Das, Kiranmoy; Pareek, Bhuvanesh; Brown, Sarah; Ghosh, Pulak: A semi-parametric Bayesian dynamic hurdle model with an application to the health and retirement study (2022)
  4. Ferraccioli, Federico; Sangalli, Laura M.; Finos, Livio: Some first inferential tools for spatial regression with differential regularization (2022)
  5. Hasan, Mirza Nazmul; Braekers, Roel: Modelling the association in bivariate survival data by using a Bernstein copula (2022)
  6. Kruse, René-Marcel; Silbersdorff, Alexander; Säfken, Benjamin: Model averaging for linear mixed models via augmented Lagrangian (2022)
  7. Li, Yehua; Qiu, Yumou; Xu, Yuhang: From multivariate to functional data analysis: fundamentals, recent developments, and emerging areas (2022)
  8. Meyer, Mark J.; Morris, Jeffrey S.; Gazes, Regina Paxton; Coull, Brent A.: Ordinal probit functional outcome regression with application to computer-use behavior in rhesus monkeys (2022)
  9. Meyer, Mary C.; Liao, Xiyue: Estimation and inference for partial linear regression surfaces using monotone warped-plane splines (2022)
  10. Vasconcelos, J. C. S.; Cordeiro, G. M.; Ortega, E. M. M.: The semiparametric regression model for bimodal data with different penalized smoothers applied to climatology, ethanol and air quality data (2022)
  11. Aydın, Dursun; Yılmaz, Ersin; Chamidah, Nur: Rational (Padé) approximation for estimating the components of the partially-linear regression model (2021)
  12. Carballo, Alba; Durban, Maria; Kauermann, Göran; Lee, Dae-Jin: A general framework for prediction in penalized regression (2021)
  13. Cheng, Changming; Bai, Erwei: Variable selection based on squared derivative averages (2021)
  14. Correia, Hannah E.; Abebe, Asheber: Regularised rank quasi-likelihood estimation for generalised additive models (2021)
  15. Cui, Erjia; Crainiceanu, Ciprian M.; Leroux, Andrew: Additive functional Cox model (2021)
  16. Emmenegger, Corinne; Bühlmann, Peter: Regularizing double machine learning in partially linear endogenous models (2021)
  17. Gressani, Oswaldo; Lambert, Philippe: Laplace approximations for fast Bayesian inference in generalized additive models based on P-splines (2021)
  18. Hattab, Mohammad W.; Ruppert, David: A mixed model approach to measurement error in semiparametric regression (2021)
  19. Hellmayr, Christoph; Gelfand, Alan E.: A partition Dirichlet process model for functional data analysis (2021)
  20. He, Yunfei; Yang, Lianqiang; Wang, Xuejun; Wang, Shijie: Adaptive local polynomial estimations for heterogeneously variational regression functions (2021)

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