Learning attribute patterns in high-dimensional structured latent attribute models
Structured latent attribute models (SLAMs) are a special family of discrete latent variable
models widely used in social and biological sciences. This paper considers the problem of
learning significant attribute patterns from a SLAM with potentially high-dimensional
configurations of the latent attributes. We address the theoretical identifiability issue,
propose a penalized likelihood method for the selection of the attribute patterns, and further
establish the selection consistency in such an overfitted SLAM with a diverging number of …
models widely used in social and biological sciences. This paper considers the problem of
learning significant attribute patterns from a SLAM with potentially high-dimensional
configurations of the latent attributes. We address the theoretical identifiability issue,
propose a penalized likelihood method for the selection of the attribute patterns, and further
establish the selection consistency in such an overfitted SLAM with a diverging number of …
Abstract
Structured latent attribute models (SLAMs) are a special family of discrete latent variable models widely used in social and biological sciences. This paper considers the problem of learning significant attribute patterns from a SLAM with potentially high-dimensional configurations of the latent attributes. We address the theoretical identifiability issue, propose a penalized likelihood method for the selection of the attribute patterns, and further establish the selection consistency in such an overfitted SLAM with a diverging number of latent patterns. The good performance of the proposed methodology is illustrated by simulation studies and two real datasets in educational assessments.
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