scholarly journals Firth's logistic regression with rare events: accurate effect estimates and predictions?

2017 ◽  
Author(s):  
Rainer Puhr ◽  
Georg Heinze ◽  
Mariana Nold ◽  
Lara Lusa ◽  
Angelika Geroldinger
Keyword(s):  
Logistic Regression ◽  
Rare Events

scholarly journals Represión del gobierno del D.F a la protesta en el periodo 2012-2014

2018 ◽  
Author(s):  
Itzel Coca Rios
Keyword(s):  
Logistic Regression ◽  
Data Analysis ◽  
Local Government ◽  
Mexico City ◽  
Rare Events ◽  
Necessary Conditions ◽  
Status Quo ◽  
Public Order ◽  
Partial Support ◽  
Nested Analysis

Between 2012 and 2014, there were ten events in Mexico City that were repressed through arbitrary arrests which affected 365 persons. Through data analysis about the protest in that period it’s verified a change in police strategy by means of more selective tactics of repression and protest disarticulation. A sample of massive demonstrations with more than 2 thousand assistants was taken to test the hypothesis of repression as a response to two main characteristics of the events: 1) a protest directed to the federal scope, that local government cannot negotiate with, and 2) that threatens public order and status quo through: violence, several claims directed to many authorities, and radical petitions. The binomial logistic regression with “rare events” package and QCA tests reveal that the federal scope of the claim and the presence of violence from the protestors are necessary conditions for the repression to occur, while radicalism and variety of claims receive partial support. The study concludes with a nested analysis of the cases of December 1st 2012 and 2013.

scholarly journals A Comparative Study of the Bias Correction Methods for Differential Item Functioning Analysis in Logistic Regression with Rare Events Data

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Marjan Faghih ◽  
Zahra Bagheri ◽  
Dejan Stevanovic ◽  
Seyyed Mohhamad Taghi Ayatollahi ◽  
Peyman Jafari
Keyword(s):  
Logistic Regression ◽  
Maximum Likelihood ◽  
Sample Size ◽  
Differential Item Functioning ◽  
Bias Correction ◽  
Rare Events ◽  
Estimation Methods ◽  
Type I ◽  
Item Functioning ◽  
Events Data

The logistic regression (LR) model for assessing differential item functioning (DIF) is highly dependent on the asymptotic sampling distributions. However, for rare events data, the maximum likelihood estimation method may be biased and the asymptotic distributions may not be reliable. In this study, the performance of the regular maximum likelihood (ML) estimation is compared with two bias correction methods including weighted logistic regression (WLR) and Firth's penalized maximum likelihood (PML) to assess DIF for imbalanced or rare events data. The power and type I error rate of the LR model for detecting DIF were investigated under different combinations of sample size, moderate and severe magnitudes of uniform DIF (DIF = 0.4 and 0.8), sample size ratio, number of items, and the imbalanced degree (τ). Indeed, as compared with WLR and for severe imbalanced degree (τ = 0.069), there were reductions of approximately 30% and 24% under DIF = 0.4 and 27% and 23% under DIF = 0.8 in the power of the PML and ML, respectively. The present study revealed that the WLR outperforms both the ML and PML estimation methods when logistic regression is used to evaluate DIF for imbalanced or rare events data.

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