Term of Award
Fall 2020
Degree Name
Doctor of Public Health in Biostatistics (Dr.P.H.)
Document Type and Release Option
Dissertation (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Biostatistics, Epidemiology, and Environmental Health Sciences
Committee Chair
Hani Samawi
Committee Member 1
Haresh Rochani
Committee Member 2
Lili Yu
Abstract
Modern research strategies rely predominantly on three steps, data collection, data analysis, and inference. In research, if the data is not collected as designed, researchers may face challenges of having incomplete data, especially when it is non-ignorable. These situations affect the subsequent steps of evaluation and make them difficult to perform. Inference with incomplete data is a challenging task in data analysis and clinical trials when missing data related to the condition under the study. Moreover, results obtained from incomplete data are prone to biases. Parameter estimation with non-ignorable missing data is even more challenging to handle and extract useful information. This dissertation proposes a method based on the influential tilting resampling approach to address non-ignorable missing data in statistical inference. This robust approach is motivated by a brief use of the importance resampling approach used by Samawi et al. (1998) for power estimation. The exponential tilting also inspires it for non-ignorable missing data proposed by Kim & Yu (2011). One of the proposed approach bases is assuming that the non-respondents' model corresponds to an exponential tilting of the respondents' model. The tilted model's specified function is the influential function of the function of interest (parameter). The other bases of the proposed approach are to use the importance resampling techniques to draw inference about some model parameters. Extensive simulation studies were conducted to investigate the performance of the proposed methods. We provided the theoretical justification, as well as application to real data.
OCLC Number
1233791318
Catalog Permalink
https://galileo-georgiasouthern.primo.exlibrisgroup.com/permalink/01GALI_GASOUTH/1r4bu70/alma9916437350502950
Recommended Citation
Gohil, Kavita, "Multiple Imputation Using Influential Exponential Tilting in Case of Non-Ignorable Missing Data" (2020). Electronic Theses and Dissertations. 2197.
https://digitalcommons.georgiasouthern.edu/etd/2197
Research Data and Supplementary Material
No
