Extended \(t\)-process regression models

DOI10.1016/J.JSPI.2017.05.006zbMath1376.62026arXiv1511.03402OpenAlexW2963695651MaRDI QIDQ2408747

Youngjo Lee, Jian-Qing Shi, Zhan-Feng Wang

Publication date: 13 October 2017

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1511.03402

zbMATH Keywords

robustnessfunctional dataGaussian process regressionselective shrinkageextended \(t\) process regression

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Nonparametric robustness (62G35)

Related Items (9)

A general robust t-process regression model ⋮ Unnamed Item ⋮ Composite T-process regression models ⋮ Bayesian estimation for the extended \(t\)-process regression models with independent errors ⋮ Estimation and prediction of a generalized mixed-effects model with \(t\)-process for longitudinal correlated binary data ⋮ A robust estimation for the extended \(t\)-process regression model ⋮ Regression analysis for multivariate process data of counts using convolved Gaussian processes ⋮ Evaluating Gaussian process metamodels and sequential designs for noisy level set estimation ⋮ Analyzing stochastic computer models: a review with opportunities

Cites Work

• Unnamed Item
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• That BLUP is a good thing: The estimation of random effects. With comments and a rejoinder by the author
• On some characterizations of the \(t\)-distribution
• Functional outlier detection with robust functional principal component analysis
• Penalized Gaussian Process Regression and Classification for High-Dimensional Nonlinear Data
• Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
• Information Consistency of Nonparametric Gaussian Process Methods
• Modelling Sparse Generalized Longitudinal Observations with Latent Gaussian Processes
• Bayesian and Non-Bayesian Analysis of the Regression Model with Multivariate Student-t Error Terms
• Generalized Gaussian Process Regression Model for Non-Gaussian Functional Data
• Nested Generalized Linear Mixed Models: An Orthodox Best Linear Unbiased Predictor Approach
• An h-Likelihood Method for Spatial Mixed Linear Models Based on Intrinsic Auto-Regressions
• Generalized Linear Models with Random Effects
• Double Hierarchical Generalized Linear Models (With Discussion)

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