A general framework for prediction in penalized regression
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Publication:5006011
DOI10.1177/1471082X19896867OpenAlexW2617272102MaRDI QIDQ5006011
Dae-Jin Lee, María Durbán, Alba Carballo, Göran Kauermann
Publication date: 12 August 2021
Published in: Statistical Modelling (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1177/1471082x19896867
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Cites Work
- Forecasting with exponential smoothing. The state space approach
- An efficient computing strategy for prediction in mixed linear models
- Efficient global optimization of expensive black-box functions
- Design and analysis of computer experiments. With comments and a rejoinder by the authors
- Bayesian computation and stochastic systems. With comments and reply.
- Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
- Smoothing and mixed models
- A note on P-spline additive models with correlated errors
- A note on smoothing parameter selection for penalized spline smoothing
- Smooth additive mixed models for predicting aboveground biomass
- On calculating with B-splines
- Flexible smoothing with P-splines: a unified approach
- A Note on Penalized Spline Smoothing With Correlated Errors
- Penalized Likelihood for General Semi-Parametric Regression Models
- Best Linear Unbiased Estimation and Prediction under a Selection Model
- Semiparametric Regression
- Smoothing and forecasting mortality rates
- Gaussian Markov Random Fields
- Simultaneous probability statements for Bayesian P-splines
- Recovery of inter-block information when block sizes are unequal
