Skip to main content
padlock icon - secure page this page is secure

A multi-index model for quantile regression with ordinal data

Buy Article:

$61.00 + tax (Refund Policy)

In this paper, we propose a quantile approach to the multi-index semiparametric model for an ordinal response variable. Permitting non-parametric transformation of the response, the proposed method achieves a root-n rate of convergence and has attractive robustness properties. Further, the proposed model allows additional indices to model the remaining correlations between covariates and the residuals from the single-index, considerably reducing the error variance and thus leading to more efficient prediction intervals (PIs). The utility of the model is demonstrated by estimating PIs for functional status of the elderly based on data from the second longitudinal study of aging. It is shown that the proposed multi-index model provides significantly narrower PIs than competing models. Our approach can be applied to other areas in which the distribution of future observations must be predicted from ordinal response data.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: dimension reduction; health economics; multi-index model; ordinal response; quantile regression

Document Type: Research Article

Affiliations: 1: Department of Statistics and Computer Information Systems, The City University of New York, One Bernard Baruch Way, Box 11-220 New York, NY, 10010, USA 2: Department of Statistics, University of Virginia, 107 Halsey Hall, P.O. Box 400135 Charlottesville, VA, 22904, USA

Publication date: June 1, 2013

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more