On Small Area Estimation Problems with Measurement Errors and Clustering
| dc.contributor.author | Torkashvand, Elaheh | |
| dc.contributor.examiningcommittee | Wang, Liqun (Statistics) Ghosh, Malay (Statistics, University of Florida) | en_US |
| dc.contributor.supervisor | Jozani, Mohammad Jafari (Statistics) Torabi, Mahmoud (Bistatistics) | en_US |
| dc.date.accessioned | 2016-10-05T19:55:12Z | |
| dc.date.available | 2016-10-05T19:55:12Z | |
| dc.date.issued | 2016 | |
| dc.degree.discipline | Statistics | en_US |
| dc.degree.level | Doctor of Philosophy (Ph.D.) | en_US |
| dc.description.abstract | In this dissertation, we first develop new statistical methodologies for small area estimation problems with measurement errors. The prediction of small area means for the unit-level regression model with the functional measurement error in the area-specific covariate is considered. We obtain the James-Stein (JS) estimate of the true area-specific covariate. Consequently, we construct the pseudo Bayes (PB) and pseudo empirical Bayes (PEB) predictors of small area means and estimate the mean squared prediction error (MSPE) associated with each predictor. Secondly, we modify the point estimation of the true area-specific covariate obtained earlier such that the histogram of the predictors of the small area means gets closer to its true one. We propose the constrained Bayes (CB) estimate of the true area-specific covariate. We show the superiority of the CB over the maximum likelihood (ML) estimate in terms of the Bayes risk. We also show the PB predictor of the small area mean based on the CB estimate of the true area-specific covariate dominates its counterpart based on the ML estimate in terms of the Bayes risk. We compare the performance of different predictors of the small area means using measures such as sensitivity, specificity, positive predictive value, negative predictive value, and MSPE. We believe that using the PEB and pseudo hierarchical Bayes predictors of small area means based on the constrained empirical Bayes (CEB) and constrained hierarchical Bayes (CHB) offers higher precision in recognizing socio-economic groups which are in danger of the prehypertension. Clustering the small areas to understand the behavior of the random effects better and accordingly, to predict the small area means is the final problem we address. We consider the Fay-Herriot model for this problem. We design a statistical test to evaluate the assumption of the equality of the variance components in different clusters. In the case of rejection of the null hypothesis of the equality of the variance components, we implement a modified version of Tukey's method. We calculate the MSPE to evaluate the effect of the clustering on the precision of predictors of the small area means. We apply our methodologies to real data sets. | en_US |
| dc.description.note | February 2017 | en_US |
| dc.identifier.citation | Torkashvand E, Jafari Jozani M, Torabi M. (2015) Pseudo-Empirical Bayes Estimation of Small Area Means Based on the James-Stein Estima- tion in Linear Regression Models with Functional Measurement Errors. Canadian Journal of Statistics 43: 265–287 | en_US |
| dc.identifier.citation | Torkashvand E., Jafari Jozani M., and Torabi M. (2016) The Constrained Bayes Estimation in Small Area Models with Functional Measurement Error. TEST: 1–21. | en_US |
| dc.identifier.citation | Torkashvand E., Jafari Jozani M., and Torabi M. (2016+) Clustering in Small Area Estimation with Area-Level Linear Mixed Models. Under Revision. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/31883 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Bates Risk, Clustering, Functional Measurement Error, Jackknife Method, Small Area Estimation | en_US |
| dc.title | On Small Area Estimation Problems with Measurement Errors and Clustering | en_US |
| dc.type | doctoral thesis | en_US |