Semiparametric Methods in the Proportional Odds Model for Ordinal Response Data with Missing Covariates
Version of Record online: 29 OCT 2010
© 2010, The International Biometric Society
Volume 67, Issue 3, pages 788–798, September 2011
How to Cite
Lee, S.-M., Gee, M.-J. and Hsieh, S.-H. (2011), Semiparametric Methods in the Proportional Odds Model for Ordinal Response Data with Missing Covariates. Biometrics, 67: 788–798. doi: 10.1111/j.1541-0420.2010.01499.x
- Issue online: 14 SEP 2011
- Version of Record online: 29 OCT 2010
- Received July 2009. Revised July 2010. Accepted July 2010.
- Conditional estimation method;
- Joint conditional method;
- Missing at random;
- Ordinal categorical data;
- Proportional odds model;
- Weighted estimator
Summary We consider the estimation problem of a proportional odds model with missing covariates. Based on the validation and nonvalidation data sets, we propose a joint conditional method that is an extension of Wang et al. (2002, Statistica Sinica 12, 555–574). The proposed method is semiparametric since it requires neither an additional model for the missingness mechanism, nor the specification of the conditional distribution of missing covariates given observed variables. Under the assumption that the observed covariates and the surrogate variable are categorical, we derived the large sample property. The simulation studies show that in various situations, the joint conditional method is more efficient than the conditional estimation method and weighted method. We also use a real data set that came from a survey of cable TV satisfaction to illustrate the approaches.