Title

Identifying Subpopulations of a Hierarchical Structured Data using a Semi-Supervised Mixture Modeling Approach

Presentation Type

Poster

Student

Yes

Track

Other

Abstract

The field of forensic statistics offers a unique hierarchical data structure in which a population is composed of several subpopulations of sources and a sample is collected from each source. This subpopulation structure creates a hierarchical layer. We propose using a semi-supervised mixture modeling approach to model the subpopulation structure which leverages the fact that we know the collection of samples came from the same, yet unknown, source. A simulation study based on a famous glass data was conducted and shows this method performs better than other unsupervised approaches which have been previously used in practice.

Start Date

2-8-2022 1:00 PM

End Date

2-8-2022 2:00 PM

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Feb 8th, 1:00 PM Feb 8th, 2:00 PM

Identifying Subpopulations of a Hierarchical Structured Data using a Semi-Supervised Mixture Modeling Approach

The field of forensic statistics offers a unique hierarchical data structure in which a population is composed of several subpopulations of sources and a sample is collected from each source. This subpopulation structure creates a hierarchical layer. We propose using a semi-supervised mixture modeling approach to model the subpopulation structure which leverages the fact that we know the collection of samples came from the same, yet unknown, source. A simulation study based on a famous glass data was conducted and shows this method performs better than other unsupervised approaches which have been previously used in practice.