Generalized Gaussian Process Regression Model for Non-Gaussian Functional Data
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Publication:4975564
DOI10.1080/01621459.2014.889021zbMath1368.62101arXiv1401.8189OpenAlexW2145648935MaRDI QIDQ4975564
Publication date: 7 August 2017
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1401.8189
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Cites Work
- Unnamed Item
- Unnamed Item
- Functional linear regression analysis for longitudinal data
- The pseudo-marginal approach for efficient Monte Carlo computations
- Posterior consistency of Gaussian process prior for nonparametric binary regression
- Linear processes in function spaces. Theory and applications
- Neural networks: A review from a statistical perspective. With comments and a rejoinder by the authors
- Convergence rate of MLE in generalized linear and nonlinear mixed-effects models: Theory and applications
- Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
- Gaussian Process Regression Analysis for Functional Data
- Generalized varying coefficient models for longitudinal data
- Information Consistency of Nonparametric Gaussian Process Methods
- Modelling Sparse Generalized Longitudinal Observations with Latent Gaussian Processes
- Adaptive Varying-Coefficient Linear Models
- Semiparametric Mean–Covariance Regression Analysis for Longitudinal Data
- Functional Varying Coefficient Models for Longitudinal Data
- Efficient Gaussian process regression for large datasets
- Gaussian Process Functional Regression Modeling for Batch Data
- Nonparametric Regression Methods for Longitudinal Data Analysis
- Functional Data Analysis for Sparse Longitudinal Data
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