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

Author

Listed:
  • Angel G. Angelov

    (Umeå University
    Sofia University St. Kliment Ohridski)

  • Magnus Ekström

    (Umeå University
    Swedish University of Agricultural Sciences)

  • 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)

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.

Suggested Citation

  • Angel G. Angelov & Magnus Ekström & Klarizze Puzon & Agustin Arcenas & Bengt Kriström, 2024. "Quantile regression with interval-censored data in questionnaire-based studies," Computational Statistics, Springer, vol. 39(2), pages 583-603, April.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:2:d:10.1007_s00180-022-01308-2
    DOI: 10.1007/s00180-022-01308-2
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    References listed on IDEAS

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    4. Belyaev, Yuri & Kriström, Bengt, 2012. "Two-step approach to Self-Selected Interval Data in Elicitation Surveys," CERE Working Papers 2012:10, CERE - the Center for Environmental and Resource Economics.
    5. Angel G. Angelov & Magnus Ekström, 2017. "Nonparametric estimation for self-selected interval data collected through a two-stage approach," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(4), pages 377-399, May.
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    8. Mickaël De Backer & Anouar El Ghouch & Ingrid Van Keilegom, 2019. "An Adapted Loss Function for Censored Quantile Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 1126-1137, July.
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    11. Angel G. Angelov & Magnus Ekström, 2019. "Maximum likelihood estimation for survey data with informative interval censoring," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(2), pages 217-236, June.
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