Article Sidebar
Published:
Jan 7, 2015Keywords:
Article Details
Open access articles published in the Journal of Agriculture, Science and Technology are under the terms of the Creative Commons Attribution (CC BY) License which permits use, distribution and reproduction in any medium, provided the original work is properly cited. The CC BY license permits commercial and non-commercial re-use of an open-access article, as long as the author is properly attributed.
Copyright on any research article published in the Journal of Agriculture, Science and Technology is retained by the author(s). The authors grant the Journal of Agriculture, Science and Technology with a license to publish the article and identify itself as the original publisher. Authors also grant any third party the right to use the article freely as long as its original authors, citation details and publisher are identified.
Use of the article in whole or in part in any medium requires proper citation as follows:
Title of Article, Names of the Author, Year of Publication, Journal Title, Volume (Issue) and page. Links to the final article on the JSRE website are encouraged.
The Creative Commons Attribution License does not affect any other rights held by authors or third parties in the article, including without limitation the rights of privacy and publicity. Use of the article must not assert or imply, whether implicitly or explicitly, any connection with, endorsement or sponsorship of such use by the author, publisher or any other party associated with the article.
For any reuse or distribution, users must include the copyright notice and make clear to others that the article is made available under a Creative Commons Attribution license, linking to the relevant Creative Commons web page. Users may impose no restrictions on the use of the article other than those imposed by the Creative Commons Attribution license.
To the fullest extent permitted by applicable law, the article is made available as is and without representation or warranties of any kind whether express, implied, statutory or otherwise and including, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of defects, accuracy, or the presence or absence of errors.
Main Article Content
The Power of Likelihood Ratio Test for a Change ‐point in Binomial Distribution
Abstract
Statistically, change point is the location or the time point such that observations follow one distribution up to the point and then another afterwards. Change point problems are encountered in our daily life and in disciplines such as economics, finance, medicine, geology among others. In this paper, the power of the likelihood ratio tests for a change point in binomial observations whose mean is dependent on explanatory variables is investigated. Artificial neural network technique is used to estimate the conditional means. These estimates are compared with ones obtained using the generalized link functions. It is shown through simulation that the power of the test increases as the size of sample. The test is found to have less power when the change point is near the edges than when the change point is at the centre. The test is also more likely to detect a change if the magnitude of the change is large. In all the instances, the neural network method is found to perform better than the parametric method.
