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.
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References in zbMATH (referenced in 625 articles , 1 standard article )
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Sorted by year (- Bishoyi, Abhishek; Wang, Xiaojing; Dey, Dipak K.: Learning semiparametric regression with missing covariates using Gaussian process models (2020)
- Kuchibhotla, Arun K.; Patra, Rohit K.: Efficient estimation in single index models through smoothing splines (2020)
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- Reiss, Philip T.; Xu, Meng: Tensor product splines and functional principal components (2020)
- Amini, Morteza; Roozbeh, Mahdi: Improving the prediction performance of the Lasso by subtracting the additive structural noises (2019)
- Cao, Jiguo; Soiaporn, Kunlaya; Carroll, Raymond J.; Ruppert, David: Modeling and prediction of multiple correlated functional outcomes (2019)
- Clairon, Quentin; Brunel, Nicolas J.-B.: Tracking for parameter and state estimation in possibly misspecified partially observed linear ordinary differential equations (2019)
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- Djeundje, Viani Biatat; Crook, Jonathan: Dynamic survival models with varying coefficients for credit risks. (2019)
- 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)
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- Huang, Lei; Jiang, Hui; Wang, Huixia: A novel partial-linear single-index model for time series data (2019)
- Hui, Francis K. C.; You, C.; Shang, H. L.; Müller, Samuel: Semiparametric regression using variational approximations (2019)
- Jiang, Yunlu; Tian, Guo-Liang; Fei, Yu: A robust and efficient estimation method for partially nonlinear models via a new MM algorithm (2019)
- 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)
- Johnson, Nels G.; Kim, Inyoung: Semiparametric approaches for matched case-control studies with error-in-covariates (2019)
- Khaled, Waled; Lin, Jinguan; Han, Zhongcheng; Zhao, Yanyong; Hao, Hongxia: Test for heteroscedasticity in partially linear regression models (2019)
- Klein, Nadja; Smith, Michael Stanley: Implicit copulas from Bayesian regularized regression smoothers (2019)
- Kneib, Thomas; Klein, Nadja; Lang, Stefan; Umlauf, Nikolaus: Modular regression -- a Lego system for building structured additive distributional regression models with tensor product interactions (2019)