Hierarchical Multiplicity Control Methods for Linear Models
Abstract
Hypothesis testing is a commonly used statistical inference technique on which a statement of the population is investigated through the evidence from a representative sample of the population. With simultaneous testing of more than one null hypotheses need for an appropriate multiple comparison method is essential. With motivation from the study of Bogomolov et al. (2017) we have modified a multiple comparison tree structure to build the required comparisons and focus on controlling the FWER (Family Wise Error Rate) using the Bonferroni procedure. The proposed method has advantages such as controlling the global error rates separately at each level, families of hypotheses at high resolution are tested only when their parent hypotheses are rejected. In this study, a level restricted method is used to control the FWER at each level and a simulation study is performed to justify the proposed method. Additionally, the proposed method was applied to two real data sets in an educational setting to make multiple comparisons
Subject Area
Statistics
Recommended Citation
Fernando, Dimuthu, "Hierarchical Multiplicity Control Methods for Linear Models" (2018). ETD Collection for University of Texas, El Paso. AAI10842608.
https://scholarworks.utep.edu/dissertations/AAI10842608