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Activity Number:
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341
- SPEED: Classification and Data Science
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #330626
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Presentation
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Title:
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Assessing Divide-and-Conquer Latent Class Analysis
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Author(s):
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Qiao Ma* and Meimeizi Zhu and Edward Mulrow
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Companies:
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NORC at the University of Chicago and NORC at the University of Chicago and NORC at the University of Chicago
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Keywords:
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Latent class analysis;
Big data;
Multivariate Categorical data;
Statistical methods;
Structural equation modeling
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Abstract:
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Latent class analysis (LCA) is generally used to construct unobserved classes from observed indicator variables. Fitting an LCA to a large dataset is challenging. Abarda et al. (2017) proposed a Divide-and-Conquer approach for LCA when dealing with big data. Divide-and-Conquer partitions a data set into multiple subsets with equal size, and fits a model to each subset. We assess LCA results in such a setting by fitting LCA models with and without the Divide-and-Conquer approach, and measure the relationships between the Bayesian Information Criteria (BIC) for the whole dataset and its subsets.
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Authors who are presenting talks have a * after their name.
