Optimum smoothing parameter selection for penalized least squares in form of linear mixed effect models
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Publication:2903134
DOI10.1080/02331934.2011.574698zbMath1245.65016OpenAlexW2030613713MaRDI QIDQ2903134
Memmedağa Memmedli, Dursun Aydın
Publication date: 23 August 2012
Published in: Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02331934.2011.574698
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Cites Work
- A statistical perspective on ill-posed inverse problems (with discussion)
- Smoothing parameter selection for smoothing splines: a simulation study
- Penalized spline estimation for functional coefficient regression models
- Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
- A comparison of regression spline smoothing procedures
- That BLUP is a good thing: The estimation of random effects. With comments and a rejoinder by the author
- Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
- Improved smoothing spline regression by combining estimates of different smoothness
- Smoothing and mixed models
- From finite sample to asymptotics: a geometric bridge for selection criteria in spline regression
- Smoothing Parameter Selection for a Class of Semiparametric Linear Models
- Simple Incorporation of Interactions into Additive Models
- A Note on Penalized Spline Smoothing With Correlated Errors
- Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion
- Incorporation of Historical Controls Using Semiparametric Mixed Models
- Semiparametric Regression
- Semiparametric Models for Longitudinal Data with Application to CD4 Cell Numbers in HIV Seroconverters
- Some Comments on C P
- Respiratory health and air pollution: additive mixed model analyses
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