A Bayesian Hurdle Quantile Regression Model for Citation Analysis with Mass Points at Lower Values

02/08/2021
by   Marzieh Shahmandi, et al.
0

Quantile regression presents a complete picture of the effects on the location, scale, and shape of the dependent variable at all points, not just the mean. We focus on two challenges for citation count analysis by quantile regression: discontinuity and substantial mass points at lower counts. A Bayesian hurdle quantile regression model for count data with a substantial mass point at zero was proposed by King and Song (2019). It uses quantile regression for modeling the nonzero data and logistic regression for modeling the probability of zeros versus nonzeros. We show that substantial mass points for low citation counts will nearly certainly also affect parameter estimation in the quantile regression part of the model, similar to a mass point at zero. We update the King and Song model by shifting the hurdle point past the main mass points. This model delivers more accurate quantile regression for moderately to highly cited articles, especially at quantiles corresponding to values just beyond the mass points, and enables estimates of the extent to which factors influence the chances that an article will be low cited. To illustrate the potential of this method, it is applied to simulated citation counts and data from Scopus.

READ FULL TEXT

page 10

page 11

page 12

page 16

page 20

page 21

page 22

research
06/16/2021

The association between topic growth and citation impact of research publications

Citations are used for research evaluation, and it is therefore importan...
research
10/28/2021

Clearing the hurdle: The mass of globular cluster systems as a function of host galaxy mass

Current observational evidence suggests that all large galaxies contain ...
research
05/09/2021

The zero-adjusted log-symmetric quantile regression model applied to extramarital affairs data

In this work, we propose a zero-adjusted log-symmetric quantile regressi...
research
04/05/2022

Bayesian Quantile Regression for Longitudinal Count Data

This work introduces Bayesian quantile regression modeling framework for...
research
12/02/2019

Factor Analysis on Citation, Using a Combined Latent and Logistic Regression Model

We propose a combined model, which integrates the latent factor model an...
research
12/28/2020

Generalized Quantile Loss for Deep Neural Networks

This note presents a simple way to add a count (or quantile) constraint ...
research
10/21/2019

A ν- support vector quantile regression model with automatic accuracy control

This paper proposes a novel 'ν-support vector quantile regression' (ν-SV...

Please sign up or login with your details

Forgot password? Click here to reset