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

Socpus ID

34447091934 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/34447091934

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