Correlates of Hunger: Evidence from the Community Based Monitoring System (CBMS) Data of Pasay City

Hunger is one of the major problems in several countries, and the objective to reduce it has become a global concern. A major initiative of the United Nations Millenium Development Goals (MDG) is the eradication of extreme poverty and hunger. However, the attempt to reduce the abovementioned issues, calls for proper identification as to who the impoverished and hungry are. There are several ways to identify the poor, but pinpointing who the hungry are, is another task

Bayesian Method with Clustering Algorithm for Credit Card Transaction Fraud Detection

Card transaction fraud is prevalent anywhere. Even with current preventive measures like the Europay, MasterCard and Visa (EMV) chips, possible weaknesses or loopholes can be exploited by fraudsters. This paper explores Naïve Bayes Classifier and clustering algorithms to detect fraud in credit card transactions. Data on fraud labels and arrival times of transactions were simulated by the Markov Modulated Poisson Process. Amounts of transactions for genuine and fraud transactions were simulated based on two Gaussian distributions. Kinds of spenders and types of fraudsters serve as the bases for the parameters used in the simulation of the data. Using the simulated data, EM clustering algorithm with three different initializations and K-means were applied to cluster transaction amounts into high, medium and low. The Naïve Bayes classifier algorithm was then applied to classify the transactions as good or fraud for the simulated data of 9 types of fraudsters across all clustering algorithms. Simulations and analyses were done using R software. Results include comparisons of true positive rates, false positive rates, and detection accuracies among the nine types of fraudsters across all clustering algorithms. For 3 clusters, (high, medium, low transaction amounts), the Naïve Bayes Method with clustering algorithms resulted to an average of 76% true positive (TP) detection, 18% false positive (FP) detection, with an overall accuracy of 81%. The same averages of TP, FP, and overall accuracy were obtained using 2 clusters (high, and low). EM clustering algorithm generated TP, FP, and overall accuracy of 80%, 16%, and 83% respectively

Dependence of mortality risks of two lives in the valuation of life annuity and life insurance functions

Most insurance companies have yet to offer products with the assumption of dependence of mortality risks of two lives. This resesarch studies the effect of dependence of the mortality risks of two lives in the valuation of life annuity and life insurance functions. Point estimates of the actuarial present values of the life annuity and life insurance functions under the dependence assumption were calculated and compared with those under the independence assumptions. Results show that the actuarial present values of the last-survivor life annuity-due functions can be as much as 10% less expensive under the dependence assumption when the two lives are less than 70 years of age. Conversely, the actuarial present values of the last-survivor life insurance functions can be as much as 144% more expensive under the dependence assumption when the two lives are less than 70 years of age

Correlates of poverty: Evidence from the community-based monitoring system (CBMS) data

This study identified correlates of poverty for Pasay City and Mogpog, Marinduque representing an urban and a rural area in the Philippines, respectively, by utilizing the 2005 census data from its Community-Based Monitoring System. Regression models with the arcsine of the square root of barangay level poverty incidence as dependent variable were investigated which allowed the identification of the correlates of poverty at the barangay level. Results showed that the significant correlates of barangay poverty incidence were average household size, proportion of households whose housing units/lot are not owned and proportion of households who own telephone/cellphone. Furthermore, lower poverty incidences were observed in barangays located in an urban area. © 2011 De La Salle University, Philippines

Bayesian mapping of poverty prevalence in the city of Pasay, Philippines, 2005

Mapping of poverty prevalence has been an essential tool which may identify high poverty incidence areas where intervention or programs from government or nongovernmental institutions are needed. To help the government achieve its goal of reducing poverty prevalence in the country, local government units should be able to implement programs to help areas of high poverty rates. This study generated poverty maps using poverty incidence that could aid in the planning of local community welfare development programs in the city of Pasay. This study also applied Bayesian hierarchical models in generating poverty prevalence using 2005 Community Based Monitoring System (CBMS) data in Pasay City. Poisson regression model was utilized to determine significant correlates and to predict poverty incidence using significant correlates