DeepAI AI Chat
Log In Sign Up

Predicting publication productivity for researchers: A latent variable model

by   Zheng Xie, et al.
National University of Defense Technology

This study provided a model for the publication dynamics of researchers, which is based on the relationship between the publication productivity of researchers and two covariates: time and historical publication quantity. The relationship allows to estimate the latent variable the publication creativity of researchers. The variable is applied to the prediction of publication productivity for researchers. The statistical significance of the relationship is validated by the high quality dblp dataset. The effectiveness of the model is testified on the dataset by the fine fittings on the quantitative distribution of researchers' publications, the evolutionary trend of their publication productivity, and the occurrence of publication events. Due to its nature of regression, the model has the potential to be extended for assessing the confidence level of prediction results, and thus has clear applicability to empirical research.


page 1

page 2

page 3

page 4


Predicting publication productivity for researchers: a piecewise Poisson model

Predicting the scientific productivity of researchers is a basic task fo...

Predicting the number of coauthors for researchers: A learning model

Predicting the number of coauthors for researchers contributes to unders...

Predicting publication productivity for authors: Shallow or deep architecture?

Academic administrators and funding agencies must predict the publicatio...

On predicting research grants productivity

Understanding the reasons associated with successful proposals is of par...

Multivariate Powered Dirichlet Hawkes Process

The publication time of a document carries a relevant information about ...

MathPSfrag: Creating Publication-Quality Labels in Mathematica Plots

This article introduces a Mathematica package providing a graphics expor...