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

Angel G. Angelov (), Magnus Ekström (), Klarizze Puzon, Agustin Arcenas and Bengt Kriström
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Angel G. Angelov: Umeå University
Magnus Ekström: Umeå University
Klarizze Puzon: United Nations University World Institute for Development Economics Research
Agustin Arcenas: University of the Philippines, Diliman
Bengt Kriström: Swedish University of Agricultural Sciences

Computational Statistics, 2024, vol. 39, issue 2, No 8, 583-603

Abstract: 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 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-022-01308-2

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