Browsing by Subject "data integration"

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    Combining non-probability and probability survey samples through mass imputation
    (Wiley, 2021-07) Kim, Jae Kwang; Park, Seho; Chen, Yilin; Wu, Changbao; Biostatistics, School of Public Health
    Analysis of non-probability survey samples requires auxiliary information at the population level. Such information may also be obtained from an existing probability survey sample from the same finite population. Mass imputation has been used in practice for combining non-probability and probability survey samples and making inferences on the parameters of interest using the information collected only in the non-probability sample for the study variables. Under the assumption that the conditional mean function from the non-probability sample can be transported to the probability sample, we establish the consistency of the mass imputation estimator and derive its asymptotic variance formula. Variance estimators are developed using either linearization or bootstrap. Finite sample performances of the mass imputation estimator are investigated through simulation studies. We also address important practical issues of the method through the analysis of a real-world non-probability survey sample collected by the Pew Research Centre.
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    HDFR: A Hydrologic Data and Modeling System with On-Demand Access to Environmental Sensing Data for Decision Making
    (IEEE, 2023-01) Luna, Daniel; Hernández, Felipe; Liang, Yao; Liang, Xu; Computer Science, Luddy School of Informatics, Computing, and Engineering
    This paper introduces the Hydrologic Disaster Forecasting and Response (HDFR), an online data and modeling integration software system that facilitates the machine-to-machine access to and the management of environmental sensing data from space and ground products. Available data sources include in-situ measurements from weather and hydrographic stations; remote sensing products from Doppler precipitation radars in the United States, Earth-monitoring satellites that measure precipitation, soil moisture, and snow cover; and numerical weather prediction model outputs from the U.S. National Weather Service. Additionally, the HDFR system provides a suite of hydrologic modeling tools; including data fusion, storm severity assessment, and hydrologic model preprocessing for the Distributed Hydrology Soil Vegetation Model (DHSVM); that are seamlessly incorporated with the diverse suite of data products. Two example workflows demonstrate how this unified framework could help bridge the gap between the online and on-demand accessing of growing wealth of Earth-observing data and hydrologic prediction for scientific and engineering applications.