While driver telematics has gained attention for risk classification in auto insurance, the scarcity of observations with telematics features has been problematic, which could be owing to either privacy concerns or adverse selection compared to the data points with traditional features. To handle this issue, we propose a data integration technique based on calibration weights. It is shown that the proposed technique can efficiently integrate the so-called traditional data and telematics data and also cope with possible adverse selection issues on the availability of telematics data. Our findings are supported by a simulation study and empirical analysis on a synthetic telematics dataset.
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Thesis advisor: Jeong, Himchan
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