A Nonparametric Test for Randomly Censored Data

Ayushee (), Narinder Kumar and Manish Goyal
Additional contact information
Ayushee: Panjab University
Narinder Kumar: Panjab University
Manish Goyal: Kurukshetra University

Annals of Data Science, 2024, vol. 11, issue 6, No 9, 2059-2075

Abstract: Abstract A nonparametric test for the testing of scale parameters, is proposed in two-sample situation with random censored data. Random censored data are mostly encountered in clinical studies, where some individuals experience the event of interest (death); some are drop-outs or loss to follow-ups and some are still alive at the end of study. The performance of test is studied by comparing it with some existing tests in terms of asymptotic relative efficiency. Critical values required for the test are computed. Statistical power of the test is assessed through simulation study with varying sample sizes and varying censoring percentages. The working of test is illustrated by applying it to a real-life data set.

Keywords: Scale test; Random censored data; Asymptotic relative efficiency; Simulation study (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40745-023-00500-5 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:aodasc:v:11:y:2024:i:6:d:10.1007_s40745-023-00500-5

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/40745

DOI: 10.1007/s40745-023-00500-5

Access Statistics for this article

Annals of Data Science is currently edited by Yong Shi

More articles in Annals of Data Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().