Contributed Poster Presentations: Statistics Without Borders

Wearable devices collect time-varying biobehavioral data, offering opportunities to investigate how behaviors influence health outcomes. However, these data often contain measurement error and excess zeros (due to nonwear, sedentary behavior, or connectivity issues), each characterized by subject-specific distributions. Current statistical methods fail to address these issues simultaneously and estimate the quantiles separately. We propose a novel estimation process that accounts for subject-specific measurement error and subject-specific time-varying zero-inflation probabilities in a functional joint linear quantile regression model. Our approach integrates a linear mixed model to adjust for measurement error and a maximum-likelihood estimation for zero inflation. Through extensive simulations, we demonstrate that our approach significantly improves estimation accuracy over methods that only address measurement error. When applied to a childhood obesity study, our approach achieves superior predictive performance and provides bootstrap-based inference, offering insights into how various predictors influence body mass index at different quantile levels. 

Data collection for food security monitoring in both developed and developing countries is increasingly conducted via phone-based surveys. In particular, the World Food Programme partners with polling agencies to contact a daily random selection of households in countries of interest. Respondents are asked a variety of questions related to demographics, diet, and other indicators of food insecurity, poverty, and household stability. While the standard application of these surveys is to simply look for long-term trends in food security, the richness of the data also invites the development of multivariate models to assess the impacts of interventions and mitigation strategies. Conventional regression methods assume temporal independence in the residual noise given that the daily samples are essentially independent, but this ignores external factors that induce autocorrelation. Adding a latent time series to the model enables such temporal correlation to be estimated and extracted, thereby improving estimation of model parameters. We demonstrate the utility of this approach in the analysis of survey data collected between 2020 and 2022.