Copula-based predictions in small area estimation
| dc.contributor.author | Grover, Kanika | |
| dc.contributor.examiningcommittee | Wang, Liqun (Statistics) Jiang, Depeng (Community Health Sciences) | en_US |
| dc.contributor.supervisor | Acar, Elif (Statistics) Torabi, Mahmoud (Statistics) | en_US |
| dc.date.accessioned | 2018-07-30T15:10:34Z | |
| dc.date.available | 2018-07-30T15:10:34Z | |
| dc.date.issued | 2018-07 | en_US |
| dc.date.submitted | 2018-07-25T16:47:49Z | en |
| dc.degree.discipline | Statistics | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | Unit-level regression models are commonly used in small area estimation to obtain empirical best linear unbiased prediction of small area characteristics. A more flexible small area estimation model has been recently proposed using the linear regression to estimate the error terms and a multivariate exchangeable copula model to characterize the error distribution within each small area. In this work, we propose a likelihood framework to estimate the intra-class dependence of the multivariate exchangeable copula for the empirical best unbiased prediction (EBUP) of small area means. Further, we propose a bootstrap approach for both parametric and semi-parametric methods to obtain a nearly unbiased estimate of the mean squared prediction error (MSPE) of the EBUP of small area means. Performance of the proposed method is evaluated through a simulation study and also by a real data application. | en_US |
| dc.description.note | October 2018 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/33183 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Best unbiased predictor | en_US |
| dc.subject | Bootstrap approach | en_US |
| dc.subject | Exchangeable copula | en_US |
| dc.subject | Pseudo likelihood | en_US |
| dc.subject | Small area estimation | en_US |
| dc.title | Copula-based predictions in small area estimation | en_US |
| dc.type | master thesis | en_US |