Bayesian inference for Pareto distribution with prior conjugate and prior non conjugate

Authors

  • Ferra YANUAR Andalas University
  • Cici Saputri Andalas University
  • Dodi Devianto Andalas University

DOI:

https://doi.org/10.20956/jmsk.v16i3.8019

Keywords:

Pareto distribution, posterior variance, absolute bias, Bayes confidence interval

Abstract

The purpose of this study is to determine the best estimator for estimating the shape   parameters of the Pareto distribution with the known  scale parameter. Estimation of these parameters is done by using the Gamma distribution as the prior distribution of the conjugate and the Uniform distribution as the non-conjugate prior distribution. A comparison of the two prior distributions is done through simulation studies with various sample sizes. The best estimator net is a method that produces the smallest posterior variance, absolute bias, and Bayes confidence interval. This study proves that the Bayes estimator by using the prior conjugate distribution produces all indicators of the goodness of the model with a smaller value than the non-conjugate prior distribution. Thus it can be concluded that the estimator with prior conjugate will produce a better predictive value than prior non-conjugate.

References

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Published

2020-04-28

How to Cite

YANUAR, F., Saputri, C., & Devianto, D. . (2020). Bayesian inference for Pareto distribution with prior conjugate and prior non conjugate. Jurnal Matematika, Statistika Dan Komputasi, 16(3), 382–390. https://doi.org/10.20956/jmsk.v16i3.8019

Issue

Vol. 16 No. 3 (2020): JMSK, MAY, 2020

Section

Research Articles

License

Copyright (c) 2020 Jurnal Matematika, Statistika dan Komputasi

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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