Model Parameters Estimation With Non-ignorable Missing Data Using Influential Exponential Tilting Resampling Approach
Document Type
Article
Publication Date
7-7-2022
Publication Title
Journal of Statistical Computation and Simulation
DOI
10.1080/00949655.2022.2097233
Abstract
This paper proposes to extend [1] mean functional estimation method based on the influential exponential tilting resampling approach (ITRA) to address non-ignorable missing data in linear model parameters statistical inference. The ITRA approach assumes that the nonrespondents’ 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 basis of the proposed approach is to use the importance resampling techniques to draw inferences about some linear model parameters. Simulation studies were conducted to investigate the performance of the proposed methods and their application to real data. Theoretical justifications are provided as well.
Recommended Citation
Gohil, Kavita, Hani M. Samawi, Haresh Rochani Dr., Lili Yu.
2022.
"Model Parameters Estimation With Non-ignorable Missing Data Using Influential Exponential Tilting Resampling Approach."
Journal of Statistical Computation and Simulation, 93 (1): 163-174: Taylor & Francis Online.
doi: 10.1080/00949655.2022.2097233
https://digitalcommons.georgiasouthern.edu/bee-facpubs/371
Comments
Georgia Southern University faculty member, Hani Samawi, Haresh Rochani, and Lili Yu co-authored Model Parameters Estimation With Non-ignorable Missing Data Using Influential Exponential Tilting Resampling Approach.