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 649 articles , 1 standard article )

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  1. Bishoyi, Abhishek; Wang, Xiaojing; Dey, Dipak K.: Learning semiparametric regression with missing covariates using Gaussian process models (2020)
  2. Cattaneo, Matias D.; Farrell, Max H.; Feng, Yingjie: Large sample properties of partitioning-based series estimators (2020)
  3. Delaigle, Aurore; Huang, Wei; Lei, Shaoke: Estimation of conditional prevalence from group testing data with missing covariates (2020)
  4. Kuchibhotla, Arun K.; Patra, Rohit K.: Efficient estimation in single index models through smoothing splines (2020)
  5. Lin, Yingqian; Tu, Yundong; Yao, Qiwei: Estimation for double-nonlinear cointegration (2020)
  6. Liu, Yusha; Li, Meng; Morris, Jeffrey S.: Function-on-scalar quantile regression with application to mass spectrometry proteomics data (2020)
  7. Martínez-Hernández, Israel; Genton, Marc G.: Recent developments in complex and spatially correlated functional data (2020)
  8. Mu, Jingru; Wang, Guannan; Wang, Li: Spatial autoregressive partially linear varying coefficient models (2020)
  9. Park, Jun Young; Polzehl, Joerg; Chatterjee, Snigdhansu; Brechmann, André; Fiecas, Mark: Semiparametric modeling of time-varying activation and connectivity in task-based fMRI data (2020)
  10. Reiss, Philip T.; Xu, Meng: Tensor product splines and functional principal components (2020)
  11. Wood, Simon N.: Inference and computation with generalized additive models and their extensions (2020)
  12. Yang, Hojin; Baladandayuthapani, Veerabhadran; Rao, Arvind U. K.; Morris, Jeffrey S.: Quantile function on scalar regression analysis for distributional data (2020)
  13. Amini, Morteza; Roozbeh, Mahdi: Improving the prediction performance of the Lasso by subtracting the additive structural noises (2019)
  14. Aydın, Dursun; Ahmed, S. Ejaz; Yılmaz, Ersin: Estimation of semiparametric regression model with right-censored high-dimensional data (2019)
  15. Cao, Jiguo; Soiaporn, Kunlaya; Carroll, Raymond J.; Ruppert, David: Modeling and prediction of multiple correlated functional outcomes (2019)
  16. Clairon, Quentin; Brunel, Nicolas J.-B.: Tracking for parameter and state estimation in possibly misspecified partially observed linear ordinary differential equations (2019)
  17. Cui, Xia; Zhao, Weihua; Lian, Heng; Liang, Hua: Pursuit of dynamic structure in quantile additive models with longitudinal data (2019)
  18. Djeundje, Viani Biatat; Crook, Jonathan: Dynamic survival models with varying coefficients for credit risks. (2019)
  19. Dziak, John J.; Coffman, Donna L.; Reimherr, Matthew; Petrovich, Justin; Li, Runze; Shiffman, Saul; Shiyko, Mariya P.: Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: interpretability for applied scientists (2019)
  20. Hosseinpouri, Mahdi; Khaledi, Majid Jafari: An area-specific stick breaking process for spatial data (2019)

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