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Keywords = Jamshidian and Jalal test

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28 pages, 4245 KiB  
Article
Regression-Based Approach to Test Missing Data Mechanisms
by Serguei Rouzinov and André Berchtold
Data 2022, 7(2), 16; https://doi.org/10.3390/data7020016 - 25 Jan 2022
Cited by 10 | Viewed by 5679
Abstract
Missing data occur in almost all surveys; in order to handle them correctly it is essential to know their type. Missing data are generally divided into three types (or generating mechanisms): missing completely at random, missing at random, and missing not at random. [...] Read more.
Missing data occur in almost all surveys; in order to handle them correctly it is essential to know their type. Missing data are generally divided into three types (or generating mechanisms): missing completely at random, missing at random, and missing not at random. The first step to understand the type of missing data generally consists in testing whether the missing data are missing completely at random or not. Several tests have been developed for that purpose, but they have difficulties when dealing with non-continuous variables and data with a low quantity of missing data. Our approach checks whether the missing data are missing completely at random or missing at random using a regression model and a distribution test, and it can be applied to continuous and categorical data. The simulation results show that our regression-based approach tends to be more sensitive to the quantity and the type of missing data than the commonly used methods. Full article
(This article belongs to the Section Information Systems and Data Management)
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