Bayesian Gaussian process factor analysis with copula for count data
作者：
Highlights：
• New method for smooth latent trajectory estimation for multivariate count data.
• Proposed method accounts for residual covariance, not explained by latent factors.
• Estimated latent trajectories are robust to noise.
• Interpretable simultaneous forecasting of several disease counts.
• Robust estimation of National Basketball Association teams latent strengths.
摘要
•New method for smooth latent trajectory estimation for multivariate count data.•Proposed method accounts for residual covariance, not explained by latent factors.•Estimated latent trajectories are robust to noise.•Interpretable simultaneous forecasting of several disease counts.•Robust estimation of National Basketball Association teams latent strengths.
论文关键词：Latent structure,Augmented likelihood,Sports,Negative binomial distribution,Gaussian process factor analysis,Gaussian copula
论文评审过程：Received 19 February 2021, Revised 14 January 2022, Accepted 3 February 2022, Available online 19 February 2022, Version of Record 2 March 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.116645
