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AIMS Mathematics

2023, Volume 8, Issue 9: 21968-21992. doi: 10.3934/math.20231120
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A new least squares method for estimation and prediction based on the cumulative Hazard function

  • 1.
    Department of Mathematics, Faculty of Science, Helwan University, Ain Helwan, Cairo, Egypt
  • 2.
    Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt
  • 3.
    Department of Accounting, College of Business Administration in Hawtat Bani Tamim, Prince Sattam Bin Abdulaziz University, Saudi Arabia
  • Received: 17 March 2023 Revised: 24 June 2023 Accepted: 04 July 2023 Published: 11 July 2023

  • MSC : 60G70, 62E20, 62F10, 62G30, 62G32, 62N05

  • In this paper, the cumulative hazard function is used to solve estimation and prediction problems for generalized ordered statistics (defined in a general setup) based on any continuous distribution. The suggested method makes use of Rényi representation. The method can be used with type Ⅱ right-censored data as well as complete data. Extensive simulation experiments are implemented to assess the efficiency of the proposed procedures. Some comparisons with the maximum likelihood (ML) and ordinary weighted least squares (WLS) methods are performed. The comparisons are based on both the root mean squared error (RMSE) and Pitman's measure of closeness (PMC). Finally, two real data sets are considered to investigate the applicability of the presented methods.

    Citation: Amany E. Aly, Magdy E. El-Adll, Haroon M. Barakat, Ramy Abdelhamid Aldallal. A new least squares method for estimation and prediction based on the cumulative Hazard function[J]. AIMS Mathematics, 2023, 8(9): 21968-21992. doi: 10.3934/math.20231120

    Related Papers:

  • In this paper, the cumulative hazard function is used to solve estimation and prediction problems for generalized ordered statistics (defined in a general setup) based on any continuous distribution. The suggested method makes use of Rényi representation. The method can be used with type Ⅱ right-censored data as well as complete data. Extensive simulation experiments are implemented to assess the efficiency of the proposed procedures. Some comparisons with the maximum likelihood (ML) and ordinary weighted least squares (WLS) methods are performed. The comparisons are based on both the root mean squared error (RMSE) and Pitman's measure of closeness (PMC). Finally, two real data sets are considered to investigate the applicability of the presented methods.


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