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

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.