Fast and Accurate Inference for the Smoothing Parameter in Semiparametric Models
DOI10.1111/anzs.12008zbMath1334.62065OpenAlexW2020090527MaRDI QIDQ2802824
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Publication date: 27 April 2016
Published in: Australian & New Zealand Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/anzs.12008
saddlepoint approximationestimating equationrestricted maximum likelihoodpartially linear modelbootstrap confidence intervalgeneralised cross-validationpenalised spline regression
Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) Nonparametric tolerance and confidence regions (62G15) Nonparametric statistical resampling methods (62G09)
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- Bandwidth choice for nonparametric regression
- Smoothing parameter selection for smoothing splines: a simulation study
- Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
- The BLUPs are not best when it comes to bootstrapping
- Semiparametric regression during 2003--2007
- Statistical predictor identification
- ON SEMIPARAMETRIC REGRESSION WITH O'SULLIVAN PENALIZED SPLINES
- A COMPARISON OF MIXED MODEL SPLINES FOR CURVE FITTING
- Saddle point approximation for the distribution of the sum of independent random variables
- An optimal selection of regression variables
- Semiparametric Regression
- SiZer for Exploration of Structures in Curves
- Likelihood Ratio Tests in Linear Mixed Models with One Variance Component
- Miscellanea. On the optimal amount of smoothing in penalised spline regression
- Exact likelihood ratio tests for penalised splines
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