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 586 articles , 1 standard article )
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Sorted by year (- Wyszynski, Karol; Marra, Giampiero: Sample selection models for count data in R (2018)
- Xiao, Luo; Li, Cai; Checkley, William; Crainiceanu, Ciprian: Fast covariance estimation for sparse functional data (2018)
- Xiyue Liao, Mary C. Meyer: cgam: An R Package for the Constrained Generalized Additive Model (2018) arXiv
- Xu, Yuhang; Li, Yehua; Nettleton, Dan: Nested hierarchical functional data modeling and inference for the analysis of functional plant phenotypes (2018)
- Dlugosz, Stephan; Mammen, Enno; Wilke, Ralf A.: Generalized partially linear regression with misclassified data and an application to labour market transitions (2017)
- Donat, Francesco; Marra, Giampiero: Semi-parametric bivariate polychotomous ordinal regression (2017)
- Ferrara, Giancarlo; Vidoli, Francesco: Semiparametric stochastic frontier models: a generalized additive model approach (2017)
- Fisher, Leigh; Wakefield, Jon; Bauer, Cici; Self, Steve: Time series modeling of pathogen-specific disease probabilities with subsampled data (2017)
- Gertheiss, Jan; Goldsmith, Jeff; Staicu, Ana-Maria: A note on modeling sparse exponential-family functional response curves (2017)
- Goldman, Matt; Kaplan, David M.: Fractional order statistic approximation for nonparametric conditional quantile inference (2017)
- Holland, Ashley D.: Penalized spline estimation in the partially linear model (2017)
- Ivanescu, Andrada E.: Adaptive inference for the bivariate mean function in functional data (2017)
- Jeong, Seonghyun; Park, Minjae; Park, Taeyoung: Analysis of binary longitudinal data with time-varying effects (2017)
- Liao, Xiyue; Meyer, Mary C.: Change-point estimation using shape-restricted regression splines (2017)
- Liu, Chong; Ray, Surajit; Hooker, Giles: Functional principal component analysis of spatially correlated data (2017)
- Malloy, Elisabeth J.; Kapellusch, Jay M.; Garg, Arun: Estimating and interpreting effects from nonlinear exposure-response curves in occupational cohorts using truncated power basis expansions and penalized splines (2017)
- Mamouridis, Valeria; Klein, Nadja; Kneib, Thomas; Cadarso Suarez, Carmen; Maynou, Francesc: Structured additive distributional regression for analysing landings per unit effort in fisheries research (2017)
- Marra, Giampiero; Radice, Rosalba: A joint regression modeling framework for analyzing bivariate binary data in (\mathsfR) (2017)
- Marra, Giampiero; Radice, Rosalba: Bivariate copula additive models for location, scale and shape (2017)
- Mhalla, Linda; Chavez-Demoulin, Valérie; Naveau, Philippe: Non-linear models for extremal dependence (2017)