# Robust density power divergence estimates for panel data models

@article{Mandal2021RobustDP,
title={Robust density power divergence estimates for panel data models},
author={Abhijit Mandal and Beste Hamiye Beyaztas and Soutir Bandyopadhyay},
journal={Annals of the Institute of Statistical Mathematics},
year={2021}
}
• Published 5 August 2021
• Computer Science
• Annals of the Institute of Statistical Mathematics
The panel data regression models have become one of the most widely applied statistical approaches in different fields of research, including social, behavioral, environmental sciences, and econometrics. However, traditional least-squares-based techniques frequently used for panel data models are vulnerable to the adverse effects of the data contamination or outlying observations that may result in biased and inefficient estimates and misleading statistical inference. In this study, we propose…

## References

SHOWING 1-10 OF 48 REFERENCES

• Computer Science
Statistics in medicine
• 2020
This work proposes a new, weighted likelihood based robust estimation procedure for linear panel data models with fixed and random effects and demonstrates that the proposed estimators show significantly better performances over the traditional methods in the presence of outliers and produce competitive results to the OLS based estimates when no outliers are present in the dataset.
• Mathematics, Economics
Comput. Stat. Data Anal.
• 2013
Considering several robust estimation methods applied to the transformed data, the robust and asymptotic properties of the proposed estimators are derived, including their breakdown points and ascyptotic distributions.
• Economics, Mathematics
• 2014
The panel data models are becoming more common in relation to cross-section and time series models for innumerable present advantages, in addition to the computational advance that facilitated theirs
• Economics
• 2007
The aim of this work is to study robust regression techniques in the fixed effects linear panel data framework by means of breakdown point computations and simulation experiments, and to show the potential of robust panel data methods.
• Mathematics, Economics
• 2015
In recent years, robust estimators for fixed effect panel data model have been developed to provide alternatives to the least square estimates in the presence of outliers. The robust adaptation
Regression model with fixed and random effects estimated by modified versions of the Ordinary Least Squares (OLS) is a standard tool of panel data analysis. However, it is vulnerable to the bad
• Mathematics
• 1969
A mixed model of regression with error components is proposed as one of possible interest for combining cross section and time series data. For known variances, it is shown that Aitken estimators and
• Mathematics
Biometrics
• 1982
A unified approach to fitting these models, based on a combination of empirical Bayes and maximum likelihood estimation of model parameters and using the EM algorithm, is discussed.
• Mathematics
• 2002
This paper introduces a new class of robust estimators for the linear regression model. They are weighted least squares estimators, with weights adaptively computed using the empirical distribution
A new class of robust regression estimators is proposed that forms an alternative to traditional robust one-step estimators and that achieves the $\sqrt{n}$ rate of convergence irrespective of the