Journal of Data Science

Abstract: Quick identification of severe injury crashes can help Emergency Medical Services (EMS) better allocate their scarce resources to improve the survival of severely injured crash victims by providing them with a fast and timely response. Data broadcast from a vehicle’s Event Data Recorder (EDR) provide an opportunity to capture crash information and send them to EMS near real-time. A key feature of EDR data is a longitudinal measure of crash deceleration. We used functional data analysis (FDA) to ascertain key features of the deceleration trajectories (absolute integral, absolute in- tegral of its slope, and residual variance) to develop and verify a risk predic- tion model for serious (AIS 3+) injuries. We used data from the 2002-2012 EDR reports and the National Highway and National Automotive Sampling System (NASS) Crashworthiness Data System (CDS) datasets available on the National Transportation Safety Administration (NHTSA) website. We consider a variety of approaches to model deceleration data, including non- penalized and penalized splines and a variable selection method, ultimately obtaining a model with a weighted AUC of 0.93. A novel feature of our approach is the use of residual variance as a measure of predictive risk. Our model can be viewed as an important first step towards developing a real- time prediction model capable of predicting the risk of severe injury in any motor vehicle crash.

Abstract: Identification of representative regimes of wave height and direction under different wind conditions is complicated by issues that relate to the specification of the joint distribution of variables that are defined on linear and circular supports and the occurrence of missing values. We take a latent-class approach and jointly model wave and wind data by a finite mixture of conditionally independent Gamma and von Mises distributions. Maximum-likelihood estimates of parameters are obtained by exploiting a suitable EM algorithm that allows for missing data. The proposed model is validated on hourly marine data obtained from a buoy and two tide gauges in the Adriatic Sea.

Matlab, Python and R have all been used successfully in teaching college students fundamentals of mathematics & statistics. In today’s data driven environment, the study of data through big data analytics is very powerful, especially for the purpose of decision making and using data statistically in this data rich environment. MatLab can be used to teach introductory mathematics such as calculus and statistics. Both Python and R can be used to make decisions involving big data. On the one hand, Python is perfect for teaching introductory statistics in a data rich environment. On the other hand, while R is a little more involved, there are many customizable programs that can make somewhat involved decisions in the context of prepackaged, preprogrammed statistical analysis.

Abstract: Student retention is an important issue for all university policy makers due to the potential negative impact on the image of the university and the career path of the dropouts. Although this issue has been thoroughly studied by many institutional researchers using parametric techniques, such as regression analysis and logit modeling, this article attempts to bring in a new perspective by exploring the issue with the use of three data mining techniques, namely, classification trees, multivariate adaptive regression splines (MARS), and neural networks. Data mining procedures identify transferred hours, residency, and ethnicity as crucial factors to retention. Carrying transferred hours into the university implies that the students have taken college level classes somewhere else, suggesting that they are more academically prepared for university study than those who have no transferred hours. Although residency was found to be a crucial predictor to retention, one should not go too far as to interpret this finding that retention is affected by proximity to the university location. Instead, this is a typical example of Simpson’s Paradox. The geographical information system analysis indicates that non-residents from the east coast tend to be more persistent in enrollment than their west coast schoolmates.

Anemia, especially among children, is a serious public health problem in Bangladesh. Apart from understanding the factors associated with anemia, it may be of interest to know the likelihood of anemia given the factors. Prediction of disease status is a key to community and health service policy making as well as forecasting for resource planning. We considered machine learning (ML) algorithms to predict the anemia status among children (under five years) using common risk factors as features. Data were extracted from a nationally representative cross-sectional survey- Bangladesh Demographic and Health Survey (BDHS) conducted in 2011. In this study, a sample of 2013 children were selected for whom data on all selected variables was available. We used several ML algorithms such as linear discriminant analysis (LDA), classification and regression trees (CART), k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF) and logistic regression (LR) to predict the childhood anemia status. A systematic evaluation of the algorithms was performed in terms of accuracy, sensitivity, specificity, and area under the curve (AUC). We found that the RF algorithm achieved the best classification accuracy of 68.53% with a sensitivity of 70.73%, specificity of 66.41% and AUC of 0.6857. On the other hand, the classical LR algorithm reached a classification accuracy of 62.75% with a sensitivity of 63.41%, specificity of 62.11% and AUC of 0.6276. Among all considered algorithms, the k-NN gave the least accuracy. We conclude that ML methods can be considered in addition to the classical regression techniques when the prediction of anemia is the primary focus.