  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.
