Prediction Methods for Future Record Values from Two-Parameter Kies Distribution

Authors

  • Nesreen Al-Olaimat Department of Applied Science, Ajloun College, Al-Balqa Applied University, Ajloun 26816, Jordan
  • Hatim Solayman Migdadi Department of Mathematics, Faculty of Science, The Hashemite University, Zarqa 13133, Jordan
  • Husam A. Bayoud Department of Mathematics, College of Sciences and Humanities, Fahad Bin Sultan University, Tabuk, Saudi Arabia
  • Mohammad Z. Raqab Department of Mathematics, Department of Mathematics, The University of Jordan, Amman 11942, Jordan

DOI:

https://doi.org/10.19139/soic-2310-5070-2123

Keywords:

Kies Distribution; Records; Maximum Likelihood Predictor; Conditional Median Predictor; Best Unbiased Predictor; Bayesian Prediction.

Abstract

In this paper, we consider the prediction problem of the future records based on observed data from two-parameter, shape and scale parameter, Kies distribution. Different point predictors including maximum likelihood, conditional median, best unbiased and Bayesian predictors of the future records are obtained. The corresponding prediction intervals using pivotal quantity, Highest Conditional Density (HCD), Shortest Length and Bayesian prediction intervals are also developed. The Monte Carlo algorithm is used to compute simulation consistent Bayesian prediction intervals for future unobserved records. The performance of the so obtained point predictors and prediction intervals are compared via experimental numerical simulation. The criteria that were considered for comparison purposes are mean square prediction error (MSPE) and prediction bias for point predictors and coverage probability (CP) and the average length (AL) for prediction intervals. A real and simulated data sets are performed for illustrative purposes.

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Published

2026-02-12

Issue

Vol. 15 No. 5 (2026)

Section

Research Articles

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How to Cite

Prediction Methods for Future Record Values from Two-Parameter Kies Distribution. (2026). Statistics, Optimization & Information Computing, 15(5), 3381-3400. https://doi.org/10.19139/soic-2310-5070-2123