Generalised Bernoulli Model for Correlated Binary Responses: Application to the National Income Dynamics Study (Nids) Datasets

Show simple item record

dc.contributor.advisor Lotsi, A.
dc.contributor.advisor Doku-Amponsah, K.
dc.contributor.author Fleku, M.
dc.contributor.other University of Ghana, College of Basic and Applied Sciences School of Physical and Mathematical Sciences Department of Statistics
dc.date.accessioned 2017-02-06T11:43:37Z
dc.date.accessioned 2017-10-13T17:39:01Z
dc.date.available 2017-02-06T11:43:37Z
dc.date.available 2017-10-13T17:39:01Z
dc.date.issued 2016-06
dc.identifier.uri http://197.255.68.203/handle/123456789/21515
dc.description Thesis(MPHIL)-University of Ghana, 2016
dc.description.abstract The bivariate Bernoulli model was used to estimate covariate parameters for conditional as well as marginal models for the NIDs datasets. This is a follow- up research on one conducted by Islam et al. (2012) which introduced the use of the bivariate Bernoulli model to properly specify the dependence among bivariate binary responses. The covariate parameters were estimated by first expressing the proposed model in the exponential family form, finding the log-likelihood function and then the corresponding estimating equations. The Newton Rahpson and the Nelder Mead methods of iteration were used to estimate the covariate parameters. The research revealed that the bivariate Bernoulli model fitted bivariate binary response data significantly better than the conditional logistic and the Generalized Estimating Equation (GEE) logistic models-marginal model. The result was same for both artificial and real-life data. It is worth mentioning that to aim at more efficient covariate estimates, the bivariate Bernoulli model is highly recommended for bivariate binary response data. However, further research is needed to choose an initial value when using the Newton Rahpson and the Nelder Mead methods of iteration, because it posed a serious challenge in this study. Again further research is recommended to probably use the multivariate Bernoulli distribution to fit multivariate binary response data and estimate covariate coefficients. en_US
dc.format.extent Ix, 126p: ill
dc.language.iso en en_US
dc.publisher University of Ghana en_US
dc.subject Generalised Bernoulli Model en_US
dc.subject Correlated Binary Responses en_US
dc.subject National Income Dynamics Study (Nids) en_US
dc.title Generalised Bernoulli Model for Correlated Binary Responses: Application to the National Income Dynamics Study (Nids) Datasets en_US
dc.type Thesis en_US
dc.rights.holder University of Ghana


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search UGSpace


Browse

My Account