Fully Nonparametric Methods for Partially Complete Data in Repeated Measures Design
| dc.contributor.advisor | Wencheko, Eshetu (professor) | |
| dc.contributor.advisor | Solomon W, Harrar (pHd) | |
| dc.contributor.author | Belina, Merga | |
| dc.date.accessioned | 2019-10-17T12:18:06Z | |
| dc.date.accessioned | 2023-11-09T14:29:19Z | |
| dc.date.available | 2019-10-17T12:18:06Z | |
| dc.date.available | 2023-11-09T14:29:19Z | |
| dc.date.issued | 2018-07-04 | |
| dc.description.abstract | In this dissertation, two related but distinct problems are studied. The first one is a fully nonparametric rank-based method for comparing samples with partially paired data. Partially-paired (correlated) data naturally arise, for example, as a result of missing values, in incomplete block designs or meta analysis. In the nonparametric setup, treatment effects are characterized in terms of functionals of distribution functions and the only assumption needed is that the marginal distributions to be non-degenerate. The setup accommodates binary, ordered categorical, discrete and continuous data in a seamless fashion. The use of nonparametric effects also addresses the Behrens-Fisher problem from the nonparametric point of view and allows construction of confidence intervals. Although, the nonparametric methods are mainly asymptotic, methods for small sample approximations are also proposed. The second problem studied is also a fully nonparametric rank-based method but for partially repeated measures data. Here a vector of nonparametric relative effect measures are defined and linear hypotheses on these effects are considered. A multitude of tests are available for hypothesis related to a vector of relative effects. We focus on asymptotic results and finite sample performance for Wald-type statistic (WTS), ANOVA-type statistic (ATS) and Multiple Comparison Test Procedure (MCTP). Notwithstanding the limitation that the theory is thoroughly investigated for the three time point case, the results can formally be extended to the more general set up but the involved expressions will be much more complicated. The finite sample behavior of the tests are investigated via simulation studies. The results provide numerical evidence of favorable performance of the nonparametric method. The new methods vi have overwhelming power advantage when treatment effects are reflected in the shape of the distribution while they perform comparably better with parametric methods for location-type alternatives. Data from a therapeutic-drug clinical trial and a randomized controlled epidemiological study are used to illustrate the application of the methods. | en_US |
| dc.identifier.uri | http://10.90.10.223:4000/handle/123456789/19470 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Nonparametric Methods | en_US |
| dc.subject | Partially Complete Data | en_US |
| dc.subject | Repeated Measures Design | en_US |
| dc.title | Fully Nonparametric Methods for Partially Complete Data in Repeated Measures Design | en_US |
| dc.type | Thesis | en_US |