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To impute or to adapt? Model specification tests’ perspective

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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.

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Acknowledgements

The authors express deep gratitude to two anonymous referees whose comments led to the improvement of the paper and opened directions for future research.

Funding

The authors of this work are supported by the Ministry of Science, Technological Development and Innovations of the Republic of Serbia (451-03-47/2023-01/ 200104). The work is also supported by the COST action CA21163 - Text, functional and other high-dimensional data in econometrics: New models, methods, applications (HiTEc).

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  1. M. Cuparić and B. Milošević have contributed equally to this work.

Authors and Affiliations

  1. Faculty of Mathematics, University of Belgrade, Studentski trg 12-16, Belgrade, 11000, Serbia

    Marija Cuparić & Bojana Milošević

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  1. Marija Cuparić
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  2. Bojana Milošević
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M. Cuparić and B. Milošević have contributed equally to this work.

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Correspondence to Bojana Milošević.

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Cuparić, M., Milošević, B. To impute or to adapt? Model specification tests’ perspective. Stat Papers 65, 1021–1039 (2024). https://doi.org/10.1007/s00362-023-01421-4

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