Bayesian Bandwidth Estimation based on Ranked Set Sampling

Document Type : Original Article

Authors
Ali Najafi Majidabadi 1
Nader Nematollahi 2
1 Department of Statistics, Allameh Tabataba'i University, Tehran, Iran
2 Department of Statistics, Allameh Tabataba'i University, Tehran, Iran.
10.22034/jirss.2024.2004038.1024
Abstract
In the estimation of a probability density function (PDF) by kernel method, two inherent problems are the choice of sampling methods and the selection of a bandwidth. In this article, we use the balanced and unbalanced ranked set sampling (RSS) methods and Bayesian bandwidth to estimate a PDF by kernel method. To compare our method with existing methods, we use an extensive simulation study to compare the RSS with simple random sampling (SRS) PDF estimator and also Bayesian bandwidth with other existing bandwidths. As an application, we use the household expenses and income data of the Statistical Center of Iran in 2021 to estimate the PDF of the total expenses of the households in Tehran province.
Keywords
Bayesian Bandwidth
Ranked Set Sampling
Probability Density Function Estimation
Simple Random Sampling
Subjects
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Journal of the Iranian Statistical Society
Volume 22, Issue 2
December 2023
Pages 97-117

  • Receive Date 05 June 2023
  • Revise Date 20 February 2024
  • Accept Date 27 April 2024