Bayesian methods to estimate sub - population

Authors

  • قتيبة نبيل نايف
  • حسين حميد خلف

DOI:

https://doi.org/10.33095/jeas.v25i110.1596

Keywords:

/ NSUM ، السكان المختبئون ، (طرق المتابعة ، أساليب كيلورث ، الحاجز وناقل الحركة)., NSUM, Hidden population, (Scale-up , Killworth, barrier & transmission’s methods)

Abstract

The aim of the research is to estimate the hidden population. Here، the number of drug users in Baghdad was calculated for the male age group (15-60) years old ، based on the Bayesian models. These models are used to treat some of the bias in the Killworth method Accredited in many countries of the world.

Four models were used: random degree، Barrier effects، Transmission bias، the first model being random، an extension of the Killworth model، adding random effects such as variance and uncertainty Through the size of the personal network، and when expanded by adding the fact that the respondents have different tendencies، the mixture of non-random variables with random to produce the model of the effects of tendencies، the other extension of the model of the degree is to add the lack of awareness of respondents to produce a model of biased Transmission bias، and for the purpose of improving it by adding other information، The composite or common model that combines the second and third methods .

The use of R software version 3.4.1 which is available on the Internet، was used. The random degree is the best model according to the data collected according to statistical questionnaire prepared for this purpose. And the results of the simulation were supportive of this conclusion، while the number of abusers according to this method 32,862 people abusers.

Downloads

Download data is not yet available.

Published

2019-02-01

Issue

Section

Statistical Researches

How to Cite

“Bayesian methods to estimate sub - population” (2019) Journal of Economics and Administrative Sciences, 25(110), p. 406. doi:10.33095/jeas.v25i110.1596.

Similar Articles

131-140 of 804

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)

1 2 > >>