A Comparative Study of Bayesian Methods for the Analysis of Binomial Proportion Data in Agricultural Research

Authors

  • Shagufta Yasmeen  Department of Statistics and Operations Research, Aligarh Muslim University, Aligarh, India
  • Athar Ali Khan  Department of Statistics and Operations Research, Aligarh Muslim University, Aligarh, India

Keywords:

Bayesian inference, independence Metropolis, JAGS, Metropolis within Gibbs, R, sampling importance resampling.

Abstract

Agricultural research incorporates data which are in the form of discrete counts or proportions based on counts. This kind of data are usually non-normally distributed that can cause issues with parameter estimation and prediction if the usual general linear model framework is applied as they do not satisfy the assumptions of linear models. Here we studied the performance of generalized linear model within Bayesian framework with some asymptotic analytic tools and simulation techniques to the analysis of proportion data with different link functions. The results obtained through the exact as well as asymptotic inference have been compared through open source software such as R and JAGS.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2017-10-31

Issue

Volume 3, Issue 6, September-October-2017

Section

Research Articles

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

How to Cite

[1]
Shagufta Yasmeen, Athar Ali Khan, " A Comparative Study of Bayesian Methods for the Analysis of Binomial Proportion Data in Agricultural Research, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 6, pp.257-268, September-October-2017.