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Activity Number:
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197
- SPAAC Poster Competition
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 8, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Government Statistics Section
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Abstract #322999
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Title:
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A Latent Class Selection Model for Categorical Response Variables with Nonignorably Missing Data
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Author(s):
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Jung Wun Lee*
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Companies:
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University of Connecticut
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Keywords:
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Latent class analysis;
Incomplete data analysis;
Selection model;
Missing not at random;
EM algorithm
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Abstract:
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We develop a new selection model for nonignorable (Missing not at random) missing values in multivariate categorical response variables by assuming that the response variables and their missingness can be summarized into categorical latent variables. Our proposed model contains two categorical latent variables. One latent variable summarizes the response patterns while the other describes the response variables' missingness. Our selection model is an alternative method to other incomplete data methods when the incomplete data mechanism is nonignorable. We implement simulation studies to evaluate the performance of the proposed method. Through the simulation studies, we discovered that our proposed method has better performances compared to other MAR/MCAR-based methods when the missingness on the data is nonignorable. In addition, we analyze the General Social Survey 2018 data to demonstrate the performance of our proposed selection model.
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Authors who are presenting talks have a * after their name.
