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The Estimation of Pareto Distribution by a Weighted Least Square Method

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Abstract

The two-parameter Pareto distribution provides reasonably good fit to the distributions of income and property value, and explains many empirical phenomena. For the censored data, the two parameters are regularly estimated by the maximum likelihood estimator, which is complicated in computation process. This investigation proposes a weighted least square estimator to estimate the parameters. Such a method is comparatively concise and easy to perceive, and could be applied to either complete or truncated data. Simulation studies are conducted in this investigation to show the feasibility of the proposed method. This report will demonstrate that the weighted least square estimator gives better performance than unweighted least square estimators with simulation cases. We also illustrate that the weighted least square estimator is very close to maximum likelihood estimator with simulation studies.

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Authors and Affiliations

  1. Department of Management Information Science, Chia-Nan University of Pharmacy and Science, Tainan, Taiwan, ROC

    Hai-Lin Lu

  2. Department of Hospital and Health Care Administration, Chia-Nan University of Pharmacy and Science, Tainan, Taiwan, ROC

    Shin-Hwa Tao

  3. 60, Erh-Jen RD., Sec.1, Jen-Te Town, Tainan County, Taiwan 717, ROC

    Shin-Hwa Tao

Authors
  1. Hai-Lin Lu
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  2. Shin-Hwa Tao
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Correspondence to Shin-Hwa Tao.

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Lu, HL., Tao, SH. The Estimation of Pareto Distribution by a Weighted Least Square Method. Qual Quant 41, 913–926 (2007). https://doi.org/10.1007/s11135-007-9100-8

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  • DOI: https://doi.org/10.1007/s11135-007-9100-8

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