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Non-parametric estimation of Kullback–Leibler discrimination information based on censored data

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  • E.I., Abdul Sathar
  • K.V., Viswakala

Abstract

Kullback–Leibler discrimination information is the most well-known theoretic divergence measure between two probability distributions associated with the same experiment, which finds application in the field of information theory In the present paper, we propose non-parametric estimators for the Kullback–Leibler discrimination information for the lifetime distribution based on censored data. Asymptotic properties of the estimators are established under suitable regularity conditions. Monte-Carlo simulation studies are carried out to compare the performance of the estimators based on the mean-squared error. The method is illustrated using a real data set.

Suggested Citation

  • E.I., Abdul Sathar & K.V., Viswakala, 2019. "Non-parametric estimation of Kullback–Leibler discrimination information based on censored data," Statistics & Probability Letters, Elsevier, vol. 154(C), pages 1-1.
  • Handle: RePEc:eee:stapro:v:154:y:2019:i:c:20
    DOI: 10.1016/j.spl.2019.06.007
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    References listed on IDEAS

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