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 642 articles , 1 standard article )

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  1. Fan, Jianqing; Li, Runze: Statistical challenges with high dimensionality: feature selection in knowledge discovery (2006)
  2. Gimenez, O.; Crainiceanu, C.; Barbraud, C.; Jenouvrier, S.; Morgan, B. J. T.: Semiparametric regression in capture-recapture modeling (2006)
  3. Houseman, E. Andrés; Coull, Brent A.; Betensky, Rebecca A.: Feature-specific penalized latent class analysis for genomic data (2006)
  4. Houseman, E. Andrés; Coull, Brent A.; Shine, James P.: A nonstationary negative binomial time series with time-dependent covariates: enterococcus counts in Boston Harbor (2006)
  5. Jank, Wolfgang: Ascent EM for fast and global solutions to finite mixtures: An application to curve-clustering of online auctions (2006)
  6. Jank, Wolfgang: The EM algorithm, its randomized implementation and global optimization: some challenges and opportunities for operations research (2006)
  7. Kanamori, Takafumi; Takeuchi, Ichiro: Conditional mean estimation under asymmetric and heteroscedastic error by linear combination of quantile regressions (2006)
  8. Kauermann, Göran: Nonparametric models and their estimation (2006)
  9. Kauermann, Göran; Khomski, Pavel: Additive two-way hazards model with varying coefficients (2006)
  10. Kneib, Thomas: Mixed model-based inference in geoadditive hazard regression for interval-censored survival times (2006)
  11. Kneib, Thomas; Fahrmeir, Ludwig: Structured additive regression for categorical space-time data: a mixed model approach (2006)
  12. Krivobokova, Tatyana; Kauermann, Göran; Archontakis, Theofanis: Estimating the term structure of interest rates using penalized splines (2006)
  13. Lee, Youngjo; Nelder, John A.; Pawitan, Yudi: Generalized linear models with random effects: unified analysis via (h)-likelihood. With CD-ROM. (2006)
  14. Liang, Hua: Checking linearity of non-parametric component in partially linear models with an application in systemic inflammatory response syndrome study (2006)
  15. Liang, Hua: Checking linearity of non-parametric component in partially linear models with an application in systemic inflammatory response syndrome study (2006)
  16. Lin, Jiang; Zhang, Daowen; Davidian, Marie: Smoothing spline-based score tests for proportional hazards models (2006)
  17. Ma, Yanyuan; Chiou, Jeng-Min; Wang, Naisyin: Efficient semiparametric estimator for heteroscedastic partially linear models (2006)
  18. Nott, David: Semiparametric estimation of mean and variance functions for non-Gaussian data (2006)
  19. Ouyang, Desheng; Li, Dong; Li, Qi: Cross-validation and non-parametric (k) nearest-neighbour estimation (2006)
  20. Price, Karen L.; Seaman, John W.: Bayesian modeling of retrospective time-to-pregnancy data with digit preference bias (2006)

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