DOI: 10.3390/sym16020212 ISSN: 2073-8994

Statistical Inference of Normal Distribution Based on Several Divergence Measures: A Comparative Study

Suad Alhihi, Maalee Almheidat, Ghassan Abufoudeh, Raed Abu Awwad, Samer Alokaily, Ayat Almomani
  • Physics and Astronomy (miscellaneous)
  • General Mathematics
  • Chemistry (miscellaneous)
  • Computer Science (miscellaneous)

Statistical predictive analysis is a very useful tool for predicting future observations. Previous literature has addressed both Bayesian and non-Bayesian predictive distributions of future statistics based on past sufficient statistics. This study focused on evaluating Bayesian and Wald predictive-density functions of a future statistic V based on a past sufficient statistic W obtained from a normal distribution. Several divergence measures were used to assess the closeness of the predictive densities to the future density. The difference between these divergence measures was investigated, using a simulation study. A comparison between the two predictive densities was examined, based on the power of a test. The application of a real data set was used to illustrate the results in this article.

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