A general robust t-process regression model
From MaRDI portal
Publication:829720
DOI10.1016/j.csda.2020.107093OpenAlexW3088123224MaRDI QIDQ829720
Youngjo Lee, Maengseok Noh, Jian-Qing Shi, Zhan-Feng Wang
Publication date: 6 May 2021
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2020.107093
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Statistical modelling of survival data with random effects. H-likelihood approach
- Extended \(t\)-process regression models
- On posterior consistency in nonparametric regression problems
- Penalized Gaussian Process Regression and Classification for High-Dimensional Nonlinear Data
- Frequentist inference on random effects based on summarizability
- Approximations for Standard Errors of Estimators of Fixed and Random Effect in Mixed Linear Models
- Information Consistency of Nonparametric Gaussian Process Methods
- Generalized Gaussian Process Regression Model for Non-Gaussian Functional Data
- Data Analysis Using Hierarchical Generalized Linear Models With R
- Gaussian Process Functional Regression Modeling for Batch Data
- Double Hierarchical Generalized Linear Models (With Discussion)
This page was built for publication: A general robust t-process regression model