The Online Chauffeured Service Demand (OCSD) research is an exploratory market study of designated driver services in China. Researchers are interested in the influencing factors of chauffeured service adoption and usage and have collected relevant data using a self-reported questionnaire. As self-reported count measure data is typically inflated, there exist challenges to its validity, which may bias estimation and increase error in empirical research. Motivated by the analysis of self-reported data with multiple inflated values, we propose a novel approach to simultaneously achieve data-driven inflated value selection and identification of important influencing factors. In particular, the regularization technique is applied to the mixing proportions of inflated values and the regression parameters to obtain shrinkage estimates. We analyze the OCSD data with the proposed approach, deriving insights into the determinants impacting service demand. The proper interpretations and implications contribute to service promotion and related policy optimization. Extensive simulation studies and consistent asymptotic properties further establish the effectiveness of the proposed approach.

翻译：在线代驾服务需求研究旨在探索中国代驾服务市场的特征，研究者通过自报问卷收集数据，以识别代驾服务采纳与使用的影响因素。由于自报计数数据通常存在膨胀问题，其有效性面临挑战，可能导致实证研究中的估计偏差和误差增加。针对具有多次膨胀值的自报数据分析需求，本文提出一种新方法，可同时实现数据驱动的膨胀值选择与重要影响因素识别。具体而言，通过将正则化技术应用于膨胀值的混合比例和回归参数，获得收缩估计值。采用该方法分析在线代驾服务需求数据，揭示影响服务需求的关键决定因素。合理的解释与启示有助于服务推广及相关政策优化。大量仿真研究与一致性渐近性质进一步验证了该方法的有效性。