Journal of Data Science

In the evolving field of survey research, leveraging machine learning to predict response behavior has transformative potential for the efficiency of survey operations. Integrating multiple data sources may improve response prediction by providing more nuanced insights for household outreach. This study presents a model-driven approach to enhancing respondent cooperation in the Medical Expenditure Panel Survey (MEPS) by combining features from disparate data sources. MEPS is a longitudinal household survey with 5 rounds of interviewing over 2.5 years. Its sample is derived prior National Health Interview Survey (NHIS) participants. MEPS Round 1 response rates are critical for sustaining representativeness throughout each panel. In this study, we constructed a multimodal machine learning model to predict (1) the likelihood of a positive response for an upcoming contact attempt and (2) the likelihood that new panel households complete a Round 1 interview. The model integrates tract-level data from the American Community Survey (ACS), outcomes from the Advance Call Records (ACR) made prior to MEPS Round 1, and paradata from the early contact period. We also explored the relative contributions of these sources to model performance. Our model aims to help manage field labor by identifying complex cases needing specialized support. It can also assist in determining the optimal mode for the next contact to increase the chance of a completed interview. Beyond improving MEPS operations, this study offers a roadmap for incorporating additional data sources to support fieldwork.

Spatial data display correlation between observations collected at nearby locations. Generally, machine and deep learning methods either do not account for this correlation or do so indirectly through correlated features. To account for spatial correlation, we propose preprocessing the data using a spatial decorrelation transform motivated from properties of a multivariate Gaussian distribution and Vecchia approximations. The preprocessed, transformed data can then be ported into a machine or deep learning tool. After model fitting on the transformed data, the output can be spatially re-correlated via the corresponding inverse transformation. We show that including this spatial adjustment results in higher predictive accuracy on simulated and real spatial datasets.

Large or very large spatial (and spatio-temporal) datasets have become common place in many environmental and climate studies. These data are often collected in non-Euclidean spaces (such as the planet Earth) and they often present nonstationary anisotropies. This paper proposes a generic approach to model Gaussian Random Fields (GRFs) on compact Riemannian manifolds that bridges the gap between existing works on nonstationary GRFs and random fields on manifolds. This approach can be applied to any smooth compact manifolds, and in particular to any compact surface. By defining a Riemannian metric that accounts for the preferential directions of correlation, our approach yields an interpretation of the nonstationary geometric anisotropies as resulting from local deformations of the domain. We provide scalable algorithms for the estimation of the parameters and for optimal prediction by kriging and simulation able to tackle very large grids. Stationary and nonstationary illustrations are provided.

We describe our implementation of the multivariate Matérn model for multivariate spatial datasets, using Vecchia’s approximation and a Fisher scoring optimization algorithm. We consider various pararameterizations for the multivariate Matérn that have been proposed in the literature for ensuring model validity, as well as an unconstrained model. A strength of our study is that the code is tested on many real-world multivariate spatial datasets. We use it to study the effect of ordering and conditioning in Vecchia’s approximation and the restrictions imposed by the various parameterizations. We also consider a model in which co-located nuggets are correlated across components and find that forcing this cross-component nugget correlation to be zero can have a serious impact on the other model parameters, so we suggest allowing cross-component correlation in co-located nugget terms.

Abstract: This paper evaluates the efficacy of a machine learning approach to data fusion using convolved multi-output Gaussian processes in the context of geological resource modeling. It empirically demonstrates that information integration across multiple information sources leads to superior estimates of all the quantities being modeled, compared to modeling them individually. Convolved multi-output Gaussian processes provide a powerful approach for simultaneous modeling of multiple quantities of interest while taking correlations between these quantities into consideration. Experiments are performed on large scale data taken from a mining context.