Tatjana Miljkovic

Modeling Impact of Hurricane Damages on Income Distribution in the Coastal U.S.

This article examines the impact of catastrophic hurricane events on income distribution in hurricane states in the United States. Media claims have been made and the perception created that the most damaging impact of hurricanes is on the lowest income population in the affected states. If these claims are true, they may have serious implications for the insurance industry and government policy makers. We develop a panel data, fixed effects econometric model that includes hurricane-impacted states as cross-sections using annual data for a period of almost 100xa0years. The Gini coefficient is used as a measure of income inequality, and is a function of normalized hurricane economic damages, gross domestic product (GDP), a set of socioeconomic variables that serves as a control, time trend, and cross-sectional dummy variables. Findings indicate that for every 100 billion US dollars in hurricane economic damages there is an increase in income inequality by 5.4xa0% as measured by Gini coefficient. Political, sociodemographic, and economic variables are also significant. These include such variables as the political party controlling the U.S. Senate, the proportion of nonwhite population by state, and GDP. Time trend is a positive and significant variable, suggesting an increase in income inequality over time. There are significant differences among the states included in the study. Our results demonstrate that different segments of the population are differently impacted by hurricanes and suggest how that differential impact could be considered in future government policies and business decisions, particularly those made by the insurance industry.

Modeling veterans' health benefit grants using the expectation maximization algorithm

A novel application of the expectation maximization (EM) algorithm is proposed for modeling right-censored multiple regression. Parameter estimates, variability assessment, and model selection are summarized in a multiple regression settings assuming a normal model. The performance of this method is assessed through a simulation study. New formulas for measuring model utility and diagnostics are derived based on the EM algorithm. They include reconstructed coefficient of determination and influence diagnostics based on a one-step deletion method. A real data set, provided by North Dakota Department of Veterans Affairs, is modeled using the proposed methodology. Empirical findings should be of benefit to government policy-makers.

Redefining standards for body mass index of the US population based on BRFSS data using mixtures

ABSTRACT Using body mass index (BMI) data from 2012 Behavioral Risk Factor Surveillance System, we test a spectrum of single parametric skewed distributions as well as Gaussian mixture densities to determine best distributional fit. We find that a k-component Gaussian mixture is the best model to describe the distribution of BMI data for the overall US population and for the population divided by gender, race, and region. A 4-component Gaussian mixture with the following sub-population means (standard deviations) fits best the US population: , , , with corresponding weights: 23%, 25%, 37%, and 15%. Current obesity standards are set based on a convention and they are fairly dated. Overweight population has BMI (25.0, 29.9). Obese population is subdivided into three grades based on BMI: grade 1 (30–35), grade 2 (35–40), grade 3 (40 and above). Our study shows that modeling BMI using mixtures can be used to redefine current standards and support them with actual prevalence rather than a dated convention. By redefining BMI standards and employing the mixture models by gender and race, health and food policy makers will have opportunity to diversify policies and treatments of obesity as premier public health problem in the USA.

Modeling Impact of Natural Hazard-Induced Disasters on Income Distribution in the United States

Economic damage due to hurricane activities has been shown to impact income inequality in the coastal states of the United States. We consider 17 other natural hazards, in addition to hurricanes, that affected the entire United States for the period 1970–2013. Two fixed effects models were developed to quantify the relationship between income inequality and economic and demographic variables, including crop and property losses from natural hazard-induced disasters. These models include state-by-year and region-by-year fixed effects models. Our findings show that the damages from all natural hazards impact income distribution across the United States, not only in hurricane-affected areas, but also in non-hurricane states. The results of our study have important implications for the insurance industry and government policymakers.

An evaluation of the reconstructed coefficient of determination and potential adjustments

ABSTRACT Previously, a method was proposed for calculating a reconstructed coefficient of determination in the case of right-censored regression using the expectation–maximization (EM) algorithm. This measure is assessed via simulation study for the purpose of evaluating the utility of model fit. Further, several reconstructed adjusted coefficients of determination are proposed and compared via simulation study for the purpose of model selection. The application of these proposed measures is illustrated on a real dataset.