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 640 articles , 1 standard article )
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Sorted by year (- Klein, Nadja; Smith, Michael Stanley: Implicit copulas from Bayesian regularized regression smoothers (2019)
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- Lee, Wonyul; Miranda, Michelle F.; Rausch, Philip; Baladandayuthapani, Veerabhadran; Fazio, Massimo; Downs, J. Crawford; Morris, Jeffrey S.: Bayesian semiparametric functional mixed models for serially correlated functional data, with application to glaucoma data (2019)
- Li, Chin-Shang; Lee, Shen-Ming; Yeh, Ming-Shan: A test for lack-of-fit of zero-inflated negative binomial models (2019)
- Li, Jinqing; Ma, Jun: Maximum penalized likelihood estimation of additive hazards models with partly interval censoring (2019)
- Li, Kan; Luo, Sheng: Bayesian functional joint models for multivariate longitudinal and time-to-event data (2019)
- McLean, M. W.; Wand, M. P.: Variational message passing for elaborate response regression models (2019)
- Neykov, Matey: Isotonic regression meets Lasso (2019)
- Ni, Yang; Stingo, Francesco C.; Baladandayuthapani, Veerabhadran: Bayesian graphical regression (2019)
- Ni, Yang; Stingo, Francesco C.; Ha, Min Jin; Akbani, Rehan; Baladandayuthapani, Veerabhadran: Bayesian hierarchical varying-sparsity regression models with application to cancer proteogenomics (2019)
- Palagi, Laura: Global optimization issues in deep network regression: an overview (2019)
- Park, Ju-Hyun; Kyung, Minjung: Bayesian curve fitting and clustering with Dirichlet process mixture models for microarray data (2019)
- Rodrigues, T.; Dortet-Bernadet, J.-L.; Fan, Y.: Simultaneous Fitting of Bayesian penalised quantile splines (2019)
- Rodríguez-Álvarez, María Xosé; Durban, Maria; Lee, Dae-Jin; Eilers, Paul H. C.: On the estimation of variance parameters in non-standard generalised linear mixed models: application to penalised smoothing (2019)
- Sadhanala, Veeranjaneyulu; Tibshirani, Ryan J.: Additive models with trend filtering (2019)
- Sakamoto, Wataru: Bias-reduced marginal Akaike information criteria based on a Monte Carlo method for linear mixed-effects models (2019)
- Seongil Jo; Taeryon Choi; Beomjo Park; Peter Lenk: bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors (2019) not zbMATH
- Shi, Jian; Liu, Anna; Wang, Yuedong: Spline density estimation and inference with model-based penalties (2019)
- Thaden, Hauke; Klein, Nadja; Kneib, Thomas: Multivariate effect priors in bivariate semiparametric recursive Gaussian models (2019)
- Wang, Binhuan; Fang, Yixin; Lian, Heng; Liang, Hua: Additive partially linear models for massive heterogeneous data (2019)