Title

Session 8: Semi-supervised Clustering of Time-dependent Categorical Sequences with Application to Discovering Education-based Life Patterns

Presenter Information/ Coauthors Information

Yingying Zhang, University of South AlabamaFollow

Presentation Type

Oral

Track

Methodology

Abstract

A new approach to the analysis of heterogeneous categorical sequences is proposed. The first-order Markov model is employed in a finite mixture setting with initial state and transition probabilities being expressed as functions of time. The expectation-maximization algorithm approach to parameter estimation is implemented in the presence of positive equivalence constraints that determine which observations must be placed in the same class in the solution. The proposed model is applied to a dataset from the British Household Panel Survey to evaluate the association between the education background and life outcomes of study participants. The analysis of the survey data reveals many interesting relationships between the level of education and major life events.

Start Date

2-8-2022 11:00 AM

End Date

2-8-2022 12:00 PM

This document is currently not available here.

Share

COinS
 
Feb 8th, 11:00 AM Feb 8th, 12:00 PM

Session 8: Semi-supervised Clustering of Time-dependent Categorical Sequences with Application to Discovering Education-based Life Patterns

Herold Crest 253 C

A new approach to the analysis of heterogeneous categorical sequences is proposed. The first-order Markov model is employed in a finite mixture setting with initial state and transition probabilities being expressed as functions of time. The expectation-maximization algorithm approach to parameter estimation is implemented in the presence of positive equivalence constraints that determine which observations must be placed in the same class in the solution. The proposed model is applied to a dataset from the British Household Panel Survey to evaluate the association between the education background and life outcomes of study participants. The analysis of the survey data reveals many interesting relationships between the level of education and major life events.