To impute or to adapt? Model specification tests’ perspective

Marija Cuparić () and Bojana Milošević ()
Additional contact information
Marija Cuparić: University of Belgrade
Bojana Milošević: University of Belgrade

Statistical Papers, 2024, vol. 65, issue 2, No 19, 1039 pages

Abstract: 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.

Keywords: Missing not at random data; Bezier curve; IPCW; Survival data; Exponentiality tests; Two-sample tests (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00362-023-01421-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stpapr:v:65:y:2024:i:2:d:10.1007_s00362-023-01421-4

Ordering information: This journal article can be ordered from
http://www.springer. ... business/journal/362

DOI: 10.1007/s00362-023-01421-4

Access Statistics for this article

Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller

More articles in Statistical Papers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().