Statistical inference based on bridge divergences

@article{Kuchibhotla2017StatisticalIB,
  title={Statistical inference based on bridge divergences},
  author={A. Kuchibhotla and S. Mukherjee and Ayanendranath Basu},
  journal={Annals of the Institute of Statistical Mathematics},
  year={2017},
  volume={71},
  pages={627-656}
}
  • A. Kuchibhotla, S. Mukherjee, Ayanendranath Basu
  • Published 2017
  • Mathematics
  • Annals of the Institute of Statistical Mathematics
  • M-estimators offer simple robust alternatives to the maximum likelihood estimator. The density power divergence (DPD) and the logarithmic density power divergence (LDPD) measures provide two classes of robust M-estimators which contain the MLE as a special case. In each of these families, the robustness of the estimator is achieved through a density power down-weighting of outlying observations. Even though the families have proved to be useful in robust inference, the relation and hierarchy… CONTINUE READING
    2 Citations

    Figures and Tables from this paper

    Projection Theorems, Estimating Equations, and Power-Law Distributions
    Unbiased Estimation Equation under $f$-Separable Bregman Distortion Measures
    • PDF

    References

    SHOWING 1-10 OF 21 REFERENCES
    Decomposable Pseudodistances and Applications in Statistical Estimation
    • 28
    • Highly Influential
    • PDF
    Inference for multivariate normal mixtures
    • 61
    • PDF
    Robust and efficient estimation by minimising a density power divergence
    • 612
    • Highly Influential
    • PDF
    A Comparison of related density-based minimum divergence estimators
    • 84
    • Highly Influential
    • PDF
    Inference based on adaptive grid selection of probability transforms
    • 3
    Robust parameter estimation with a small bias against heavy contamination
    • 168
    • Highly Influential
    • PDF
    Choosing a robustness tuning parameter
    • 71
    • Highly Influential
    Robustifying Model Fitting
    • 61
    • Highly Influential