Semiparametric Regression Using Variational Approximations

DOI10.1080/01621459.2018.1518235zbMath1428.62156arXiv1810.01949OpenAlexW3104810320WikidataQ129280276 ScholiaQ129280276MaRDI QIDQ5208080

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Publication date: 15 January 2020

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1810.01949

zbMATH Keywords

mixed modelspenalized splinesgeneralized additive modelssmoothing parameter

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08)

Related Items (6)

Ultra-Fast Approximate Inference Using Variational Functional Mixed Models ⋮ Sufficient dimension reduction for clustered data via finite mixture modelling ⋮ Fast and universal estimation of latent variable models using extended variational approximations ⋮ Fast, Approximate Maximum Likelihood Estimation of Log-Gaussian Cox Processes ⋮ A penalized least product relative error loss function based on wavelet decomposition for non-parametric multiplicative additive models ⋮ Laplace approximations for fast Bayesian inference in generalized additive models based on P-splines

Uses Software

• SemiPar
• GAMLSS
• gamair
• ConfBands

Cites Work

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• Approximate Inference in Generalized Linear Mixed Models
• Simultaneous Confidence Bands for Penalized Spline Estimators
• Strictly Proper Scoring Rules, Prediction, and Estimation
• Generalized Additive Models for Location, Scale and Shape

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