Journal of Data Science

In this paper, a comparison is provided for volatility estimation in Bayesian and frequentist settings. We compare the predictive performance of these two approaches under the generalized autoregressive conditional heteroscedasticity (GARCH) model. Our results indicate that the frequentist estimation provides better predictive potential than the Bayesian approach. The finding is contrary to some of the work in this line of research. To illustrate our finding, we used the six major foreign exchange rate datasets.

Abstract: Meta-analytic methods for diagnostic test performance, Bayesian methods in particular, have not been well developed. The most commonly used method for meta-analysis of diagnostic test performance is the Summary Receiver Operator Characteristic (SROC) curve approach of Moses, Shapiro and Littenberg. In this paper, we provide a brief summary of the SROC method, then present a case study of a Bayesian adaptation of their SROC curve method that retains the simplicity of the original model while additionally incorporating uncertainty in the parameters, and can also easily be extended to incorporate the effect of covariates. We further derive a simple transformation which facilitates prior elicitation from clinicians. The method is applied to two datasets: an assessment of computed tomography for detecting metastases in non-small-cell lung cancer, and a novel dataset to assess the diagnostic performance of endoscopic ultrasound (EUS) in the detection of biliary obstructions relative to the current gold standard of endoscopic retrograde cholangiopancreatography (ERCP).

Abstract: Particulate matter smaller than 2.5 microns (PM2.5) is a com monly measured parameter in ground-based sampling networks designed to assess short and long-term air quality. The measurement techniques for ground based PM2.5 are relatively accurate and precise, but monitoring lo cations are spatially too sparse for many applications. Aerosol Optical Depth (AOD) is a satellite based air quality measurement that can be computed for more spatial locations, but measures light attenuation by particulates throughout in entire air column, not just near the ground. The goal of this paper is to better characterize the spatio-temporal relationship between the two measurements. An informative relationship will aid in imputing PM2.5 values for health studies in a way that accounts for the variability in both sets of measurements, something physics based models cannot do. We use a data set of Chicago air quality measurements taken during 2007 and 2008 to construct a weekly hierarchical model. We also demonstrate that AOD measurements and a latent spatio-temporal process aggregated weekly can be used to aid in the prediction of PM2.5measurements.

Abstract: The assessment of modality or “bumps” in distributions is of in terest to scientists in many areas. We compare the performance of four statistical methods to test for departures from unimodality in simulations, and further illustrate the four methods using well-known ecological datasets on body mass published by Holling in 1992 to illustrate their advantages and disadvantages. Silverman’s kernel density method was found to be very conservative. The excess mass test and a Bayesian mixture model approach showed agreement among the data sets, whereas Hall and York’s test pro vided strong evidence for the existence of two or more modes in all data sets. The Bayesian mixture model also provided a way to quantify the un certainty associated with the number of modes. This work demonstrates the inherent richness of animal body mass distributions but also the difficulties for characterizing it, and ultimately understanding the processes underlying them.

The complexity of energy infrastructure at large institutions increasingly calls for data-driven monitoring of energy usage. This article presents a hybrid monitoring algorithm for detecting consumption surges using statistical hypothesis testing, leveraging the posterior distribution and its information about uncertainty to introduce randomness in the parameter estimates, while retaining the frequentist testing framework. This hybrid approach is designed to be asymptotically equivalent to the Neyman-Pearson test. We show via extensive simulation studies that the hybrid approach enjoys control over type-1 error rate even with finite sample sizes whereas the naive plug-in method tends to exceed the specified level, resulting in overpowered tests. The proposed method is applied to the natural gas usage data at the University of Connecticut.