In insurance underwriting, misrepresentation represents the type of insurance fraud when an applicant purposely makes a false statement on a risk factor that may lower his or her cost of insurance. Under the insurance ratemaking context, we propose to use the expectation-maximization (EM) algorithm to perform maximum likelihood estimation of the regression effects and the prevalence of misrepresentation for the misrepresentation model proposed by Xia and Gustafson [(2016) The Canadian Journal of Statistics, 44, 198–218]. For applying the EM algorithm, the unobserved status of misrepresentation is treated as a latent variable in the complete-data likelihood function. We derive the iterative formulas for the EM algorithm and obtain the analytical form of the Fisher information matrix for frequentist inference on the parameters of interest for lognormal losses. We implement the algorithm and demonstrate that valid inference can be obtained on the risk effect despite the unobserved status of misrepresentation. Applying the proposed algorithm, we perform a loss severity analysis with the Medical Expenditure Panel Survey data. The analysis reveals not only the potential impact misrepresentation may have on the risk effect but also statistical evidence on the presence of misrepresentation in the self-reported insurance status.

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These results support the importance of duration of ruptured membranes as a risk factor for vertical transmission of HIV and suggest that a diagnosis of AIDS in the mother at the time of delivery may potentiate the effect of duration of ruptured membranes.

Background: The United States experienced severe mental health budget cuts in many states across the nation during the years of the largest recession since the Great Depression. Illinois had one of the hardest hit mental health budgets in the country. The massive mental health funding cuts in Illinois, combined with the state's budget impasse, left fewer facilities available to provide treatment and support to those in need. Many of Illinois's most vulnerable populations either had reduced access, or no access to care. Serious spillover effects were felt by emergency rooms, community hospitals, and the criminal justice system. Therefore, the purpose of this research is to examine disparities in Health Related Quality of Life for those with depression after the funding cuts in Illinois. Methods: Data from the 2017 Behavior Risk Factor Surveillance System was analyzed by using multivariate logistic regression models of the Health Related Quality of Life measures for Illinoisans diagnosed with depressive disorders. Results: According to the regression models in this study, disparities exist in HRQOL for Illinoisans with depressive disorders. In all of the HRQOL models, income was associated with a reduction in HRQOL. Additionally, disparities exist in HRQOL for certain age groups and those who are unemployed. Interestingly, the models did not show any racial disparities as anticipated. Conclusion: Without the basic policy-level deficiencies addressed, disparities in Health Related Quality of Life for Illinois's most vulnerable populations will continue to exist as will costly economic spillover effects.

This study considers concurrent adjustment of misclassification and missingness in categorical covariates in regression models. Under various misclassification and missingness mechanisms, we derive a general mixture regression structure for regression models that can incorporate multiple surrogates of categorical covariates that are subject to misclassification and missingness. In simulation studies, we demonstrate that including observations with missingness and/or multiple surrogates of the covariate helps alleviate the efficiency loss caused by misclassification. In addition, we study the efficacy of misclassification adjustment when the number of categories increases for the covariate of interest. Using data from the Longitudinal Studies of HIV-Associated Lung Infections and Complications, we perform simultaneous adjustment of misclassification and missingness in the self-reported cocaine and heroin use variable when assessing its association with lung density measures.