Date of Award

Summer 2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Mathematics & Statistics

Program/Concentration

Computational and Applied Mathematics

Committee Director

Sandipan Dutta

Committee Member

Lucia Tabacu

Committee Member

Yet Nguyen

Committee Member

Sharan Asundi

Abstract

Clustered data refers to a specific kind of correlated data where units within the same cluster are correlated while units from different clusters are independent. The number of units in each cluster, known as the cluster size, can be associated with the cluster’s outcome. This is known as the informative cluster size (ICS) and affects the inference drawn from clustered data. Recently, a hypothesis testing method has been developed to detect the presence of ICS. However, considering ICS alone may not be sufficient when comparing outcomes across multiple groups of units within clustered data. The size of a group within a cluster (intra-cluster group size) can also be correlated with the outcomes of that group within the cluster, leading to an informative intra-cluster group size (IICGS). IICGS can occur even without the presence of ICS in the data. In this dissertation, a statistical hypothesis testing method specifically designed to test for IICGS in clustered data is developed. Another chapter of this dissertation involves statistical method development for clustered ordinal data. There does not exist any method to test the association between two ordinal variables in a clustered data with ICS. To address this gap, a statistical hypothesis testing procedure is developed utilizing a marginalization principle. Through simulated and real data applications, the usefulness of the proposed methods is established.

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DOI

10.25777/q4h6-td97

ISBN

9798384454533

ORCID

0000-0001-8937-5148

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