Identification of Influencing Factors on Self-reported Count Data with Multiple Potential Inflated Values

TL;DR

This paper introduces a novel statistical method to identify influential factors in self-reported count data with multiple inflated values, improving data validity and estimation accuracy in market research.

Contribution

The paper proposes a new regularization-based approach for simultaneous inflated value selection and factor identification in self-reported count data.

Findings

Applied method to OCSD data revealing key demand determinants

Demonstrated improved estimation accuracy through simulations

Provided insights for policy and service promotion

Abstract

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.