Journal of Data Science

Abstract: Li and Tiwari (2008) recently developed a corrected Z-test statistic for comparing the trends in cancer age-adjusted mortality and incidence rates across overlapping geographic regions, by properly adjusting for the correlation between the slopes of the fitted simple linear regression equations. One of their key assumptions is that the error variances have unknown but common variance. However, since the age-adjusted rates are linear combinations of mortality or incidence counts, arising naturally from an underlying Poisson process, this constant variance assumption may be violated. This paper develops a weighted-least-squares based test that incorporates heteroscedastic error variances, and thus significantly extends the work of Li and Tiwari. The proposed test generally outperforms the aforementioned test through simulations and through application to the age-adjusted mortality data from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute.

Abstract: Providing reliable estimates of the ratios of cancer incidence and mortality rates across geographic regions has been important for the National cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) Program as it profiles cancer risk factors as well decides cancer control planning. A fundamental difficulty, however, arises when such ratios have to be computed to compare the rate of a subregion (e.g., California) with that of a parent region (e.g., the US). Such a comparison is often made for policy-making purposes. Based on F-approximations as well as normal approximations, this paper provides new confidence intervals (CIs) for such rate ratios. Intensive simulations, which capture the real issues with the observed mortality data, reveal that these two CIs perform well. In general, for rare cancer sites, the F-intervals are often more conservative, and for moderate and common cancers, all intervals perform similarly.

Abstract: Although many scoring models have been developed in literature to offer financial institutions guidance in credit granting decision, the pur pose of most scoring models are to improve their discrimination ability, not their explanatory ability. Therefore, the conventional scoring models can only provide limited information in the relationship among customer de mographics, default risk, and credit card attributes, such as APR (annual percentage rate) and credit limits. In this paper, a Bayesian behavior scor ing model is proposed to help financial institutions identify factors which truly reflect customer value and can affect default risk. To illustrate the proposed model, we applied it to the credit cardholder database provided by one major bank in Taiwan. The empirical results show that increasing APR will raise the default probability greatly. Single cardholders are less accountable for credit card repayment. High income, female, or cardholders with higher education are more likely to have good repayment ability.

Abstract: Behavioral risk factors for cancer tend to cluster within individuals, which can compound risk beyond that associated with the individual risk factors alone. There has been increasing attention paid to the prevalence of multiple risk factors (MRF) for cancer, and to the importance of designing interventions that help individuals reduce their risks across multiple behaviors simultaneously. The purpose of this paper is to develop methodology to identify an optimal linear combination of multiple risk factors (score function) which would facilitate evaluation of cancer interventions.