Date of Award
Doctor of Philosophy
Dr. Jung Chao Wang
Dr. Jeffrey Terpstra
Dr. Joshua D. Naranjo
Dr. YuanLong Liu
Zero-inflated, excess zeros, two-part model
Many data have excess zeros or unobservable values falling below detection limit. For example, data on hospitalization costs incurred by members of a health insurance plan will have zeros for the percentage who did not get sick. Benzene exposure measurements on petroleum re nery workers have some exposures fall below the limit of detection. Traditional methods of inference like one-way ANOVA are not appropriate to analyze such data since the point mass at zero violates typical distribution assumptions.
For testing for equality of means of k distributions, we will propose a likelihood ratio test that accounts for excess zeros or detection limits. We will conduct simulations to study nite sample properties of the proposed procedure on both Log-normal distribution and Gamma distribution. One imputation method will be proposed as an alternative approach.
Jiang, Haolai, "lnference on Differences in k Means for Data with Excess Zeros and Detection Limits" (2014). Dissertations. 376.