Title
Comparing Four Estimation Methods For Uninsurance In Florida
Keywords
Estimation methods; Medicaid; Uninsurance
Abstract
Although the high percentage of the uninsured is an important public policy issue, discrepancies in both state and national estimates of the numbers of uninsured are reported. There is a critical need to address the methodological problem of the estimation. This study compares four advanced estimation methods for uninsurance by using Florida Health Insurance Survey data as an example. The four predictive models are decision tree, neural network, general logistic regression and two-stage logistic regression. The two-stage logistic regression model is found to be the best model for imputing missing data on health insurance. Risk factors to uninsurance are identified. Corresponding policy implications are discussed. Copyright © 2007 Inderscience Enterprises Ltd.
Publication Date
1-1-2007
Publication Title
International Journal of Public Policy
Volume
2
Issue
3-4
Number of Pages
342-355
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1504/IJPP.2007.012912
Copyright Status
Unknown
Socpus ID
34447091934 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/34447091934
STARS Citation
Zhang, Ning Jackie; Wan, Thomas T.H.; and Brent, Renee, "Comparing Four Estimation Methods For Uninsurance In Florida" (2007). Scopus Export 2000s. 7328.
https://stars.library.ucf.edu/scopus2000/7328