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:: Volume 12, Issue 2 (3-2019) ::
J. of Stat. Sci. 2019, 12(2): 469-483 Back to browse issues page
Inverse-Probability Weighting and Multiple Imputation Methods for Analyzing Missing in the Response
Freshteh Osmani , Ali Akbar Rasekhi
Abstract:   (768 Views)

Data loss and missing values is a common problem in data analysis. Therefore, it is important that by estimating missing values, the data was completed and placed in the proper path. Two approaches commonly used to deal with missing data are multiple imputation (MI) and inverse-probability weighting (IPW). In this study, a third approach which is a combination of MI and IPW will be introduced. It can be said by results of the simulation study that IPW/MI can have advantages over alternatives. Regarding the missing values in most studies, especially in the medical field, ignoring them leads to wrong analysis. So, using of robust methods to proper analysis of missing values is essential.

Keywords: Multiple Imputation, Inverse-Probability Weighting, Missingness.
Full-Text [PDF 167 kb]   (130 Downloads)    
Type of Study: Applied | Subject: Biostatistics
Received: 2015/09/24 | Accepted: 2018/05/25 | Published: 2018/10/17
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Osmani F, Rasekhi A A. Inverse-Probability Weighting and Multiple Imputation Methods for Analyzing Missing in the Response. J. of Stat. Sci.. 2019; 12 (2) :469-483
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Volume 12, Issue 2 (3-2019) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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