Functional analysis of generalized linear models under non-linear constraints with applications to identifying highly-cited papers
作者：
Highlights：
• Robust functional form contains true parameters far more often than popular models.
• Matches/outperforms widely used regression and Neural Network models.
• Finds appropriate balance between Model Fit, Inference and Prediction (MIPs).
• Introduces new large-sample DGP test; can use to improve A.I. models.
• For MIS field finds Popularity Parameter to be important for predicting citations.
摘要
•Robust functional form contains true parameters far more often than popular models.•Matches/outperforms widely used regression and Neural Network models.•Finds appropriate balance between Model Fit, Inference and Prediction (MIPs).•Introduces new large-sample DGP test; can use to improve A.I. models.•For MIS field finds Popularity Parameter to be important for predicting citations.
论文关键词：Unbalanced data,MCMC,Neural Networks,Artificial Intelligence,Machine Learning,Logistic regression,Categorical data analysis,Bayesian estimation,Model fit,Classification,Inference
论文评审过程：Received 18 April 2020, Revised 16 November 2020, Accepted 19 November 2020, Available online 16 January 2021, Version of Record 16 January 2021.
论文官网地址：https://doi.org/10.1016/j.joi.2020.101112
