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To impute or to adapt? Model specification tests’ perspective

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  • Marija Cuparić

    (University of Belgrade)

  • Bojana Milošević

    (University of Belgrade)

Abstract

We study the problem of testing a wide range of statistical hypotheses under the assumption of the sample being randomly right-censored. As an alternative to the classical approach which assumes the modification of a test statistic for complete data, we propose a novel imputation procedure. The new approach, for the first time, is completely hypothesis free which means that it does not require any modification for the application of different statistical procedures. The competitive properties are demonstrated with several goodness-of-fit tests to exponentiality, as well as the most well known two-sample tests. Finally, concluding remarks about whether it is better to impute data or to adapt statistical procedures are provided.

Suggested Citation

  • Marija Cuparić & Bojana Milošević, 2024. "To impute or to adapt? Model specification tests’ perspective," Statistical Papers, Springer, vol. 65(2), pages 1021-1039, April.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:2:d:10.1007_s00362-023-01421-4
    DOI: 10.1007/s00362-023-01421-4
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    References listed on IDEAS

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    1. Somnath Datta & Dipankar Bandyopadhyay & Glen A. Satten, 2010. "Inverse Probability of Censoring Weighted U‐statistics for Right‐Censored Data with an Application to Testing Hypotheses," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(4), pages 680-700, December.
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    6. Marija Cuparić & Bojana Milošević, 2022. "New characterization-based exponentiality tests for randomly censored data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 461-487, June.
    7. Sudheesh K. Kattumannil & P. Anisha, 2019. "A simple non-parametric test for decreasing mean time to failure," Statistical Papers, Springer, vol. 60(1), pages 73-87, February.
    8. Choongrak Kim & Byeong Park & Woochul Kim & Chiyon Lim, 2003. "Bezier curve smoothing of the Kaplan-Meier estimator," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(2), pages 359-367, June.
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