A flexible approach to inference in semiparametric regression models with correlated errors using Gaussian processes

DOI10.1016/j.csda.2016.05.010zbMath1466.62093OpenAlexW2409828733MaRDI QIDQ1659007

Thomas A. Severini, Heping He

Publication date: 15 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2016.05.010

zbMATH Keywords

semiparametric modelgeneralized least squaresrestricted maximum likelihoodGaussian process regression

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Related Items

On Some Characteristics of Gaussian Covariance Functions ⋮ Frequentist conditional variance for nonlinear mixed-effects models

Uses Software

• R
• S-PLUS
• SemiPar
• ElemStatLearn
• MEMSS
• PMTK

Cites Work

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• Integrated likelihood methods for eliminating nuisance parameters. (With comments and a rejoinder).
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• Statistical estimation in varying coefficient models
• That BLUP is a good thing: The estimation of random effects. With comments and a rejoinder by the author
• Integrated likelihood inference in semiparametric regression models
• On the application of extended quasi-likelihood to the clustered data case
• Predictive Approaches for Choosing Hyperparameters in Gaussian Processes
• Semiparametric regression for clustered data
• Penalized Gaussian Process Regression and Classification for High-Dimensional Nonlinear Data
• Gaussian Hilbert Spaces
• Semiparametric Regression
• Mixed-Effects Models in S and S-PLUS
• Extended Generalized Estimating Equations for Clustered Data
• New Local Estimation procedure for a Non-Parametric Regression Function for Longitudinal Data
• Integrated likelihood functions for non-Bayesian inference
• A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines
• Self Modeling Nonlinear Regression
• The elements of statistical learning. Data mining, inference, and prediction
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