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 586 articles , 1 standard article )

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  1. Amini, Morteza; Roozbeh, Mahdi: Improving the prediction performance of the Lasso by subtracting the additive structural noises (2019)
  2. Cao, Jiguo; Soiaporn, Kunlaya; Carroll, Raymond J.; Ruppert, David: Modeling and prediction of multiple correlated functional outcomes (2019)
  3. Cui, Xia; Zhao, Weihua; Lian, Heng; Liang, Hua: Pursuit of dynamic structure in quantile additive models with longitudinal data (2019)
  4. Djeundje, Viani Biatat; Crook, Jonathan: Dynamic survival models with varying coefficients for credit risks. (2019)
  5. Huang, Lei; Jiang, Hui; Wang, Huixia: A novel partial-linear single-index model for time series data (2019)
  6. Jian, Ling; Ma, Xiaoyu; Song, Yunquan; Luo, Shihua: Laplace error penalty-based M-type model detection for a class of high dimensional semiparametric models (2019)
  7. Li, Kan; Luo, Sheng: Bayesian functional joint models for multivariate longitudinal and time-to-event data (2019)
  8. McLean, M. W.; Wand, M. P.: Variational message passing for elaborate response regression models (2019)
  9. Neykov, Matey: Isotonic regression meets Lasso (2019)
  10. Rodrigues, T.; Dortet-Bernadet, J.-L.; Fan, Y.: Simultaneous Fitting of Bayesian penalised quantile splines (2019)
  11. Sakamoto, Wataru: Bias-reduced marginal Akaike information criteria based on a Monte Carlo method for linear mixed-effects models (2019)
  12. Seongil Jo; Taeryon Choi; Beomjo Park; Peter Lenk: bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors (2019) not zbMATH
  13. Wang, Binhuan; Fang, Yixin; Lian, Heng; Liang, Hua: Additive partially linear models for massive heterogeneous data (2019)
  14. Akdeniz, Esra; Akdeniz, Fikri; Roozbeh, Mahdi: A new difference-based weighted mixed Liu estimator in partially linear models (2018)
  15. 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)
  16. Calonico, Sebastian; Cattaneo, Matias D.; Farrell, Max H.: On the effect of bias estimation on coverage accuracy in nonparametric inference (2018)
  17. Chandra, Hukum; Salvati, Nicola; Chambers, Ray: Small area estimation under a spatially non-linear model (2018)
  18. Chatla, Suneel Babu; Shmueli, Galit: Efficient estimation of COM-Poisson regression and a generalized additive model (2018)
  19. Clairon, Quentin; Brunel, Nicolas J.-B.: Optimal control and additive perturbations help in estimating ill-posed and uncertain dynamical systems (2018)
  20. Djeundje, Viani Biatat; Crook, Jonathan: Incorporating heterogeneity and macroeconomic variables into multi-state delinquency models for credit cards (2018)

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