Quantile regression with interval-censored data in questionnaire-based studies

Abstract

Interval-censored data can arise in questionnaire-based studies when the respondent gives an answer in the form of an interval without having pre-specified ranges. Such data are called self-selected interval data. In this case, the assumption of independent censoring is not fulfilled, and therefore the ordinary methods for interval-censored data are not suitable. This paper explores a quantile regression model for self-selected interval data and suggests an estimator based on estimating equations. The consistency of the estimator is shown. Bootstrap procedures for constructing confidence intervals are considered. A simulation study indicates satisfactory performance of the proposed methods. An application to data concerning price estimates is presented.

Keywords

Interval-censored data; Dependent censoring; Self-selected interval; Quantile regression; Estimating equation

Published in

Computational Statistics
2024, volume: 39, number: 2, pages: 583–603
Publisher: SPRINGER HEIDELBERG

SLU Authors

• Ekström, Magnus

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    
  • Umeå University

• Kriström, Bengt

  • Department of Forest Economics, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Probability Theory and Statistics

Publication identifier

• DOI: https://doi.org/10.1007/s00180-022-01308-2

Permanent link to this page (URI)

https://res.slu.se/id/publ/120738