Open AccessCharacteristics of Bayes Estimator in the Geometric Distribution with Prior Beta
Author(s) -
Teguh Susilo,
Widiarti,
Dian Kurniasari,
Dorrah Aziz
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1751/1/012020
Subject(s) - minimum variance unbiased estimator , bias of an estimator , mathematics , beta distribution , statistics , estimator , bayes' theorem , efficient estimator , bayes estimator , trimmed estimator , geometric distribution , prior probability , stein's unbiased risk estimate , variance (accounting) , probability distribution , bayesian probability , accounting , business
This study aims to examine the unbiased, minimum variance (efficient), and consistent characteristics of Bayes estimator in the Geometric distribution with prior Beta. Based on the results of simulation studies it is found that the Bayes estimator in the Geometric distribution with prior Beta are symptotically unbiased estimator for values θ < 0,5 and is biased for others, are efficient for the number of samples sizes large and values θ ≤ 0,6 and not efficient for others and consistent when value θ ≤ 0,5 and inconsistent for other.