Estimation of zero‐inflated proportional odds regression with missing covariates

A zero‐inflated proportional odds (ZIPO) regression model is proposed as an alternative for analyzing ordinal categorical response data characterized by an excess of zeros beyond what a proportional odds regression model accounts for. To address the challenge of parameter estimation in the ZIPO regression model when covariates are missing at random, we recommend the use of the inverse probability weighting method and the nonparametric multiple imputation method. Variance estimation is conducted through Rubin‐type and two proposed bootstrap methods. Simulations are carried out to assess the finite‐sample performances of the Rubin‐type method and the two proposed bootstrap methods. The results of the simulations suggest that the performances of the two proposed bootstrap methods are comparable and generally superior to the Rubin‐type method, as indicated by the agreement between empirical standard deviation and empirical (or mean) asymptotic standard error. To illustrate the practical application of the proposed estimation methods, we utilize a survey data set capturing violations of traffic rules among motorcyclist respondents in Taiwan.