Testing the equality among distribution functions from independent and right censored samples via Cramér–von Mises criterion
From MaRDI portal
Publication:5124833
DOI10.1080/02664763.2010.484486OpenAlexW2060434957MaRDI QIDQ5124833
Publication date: 30 September 2020
Published in: Journal of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02664763.2010.484486
Kaplan-Meier estimatorcensored databootstrappingCramér-von Mises criterionweighted tests\(k\)-sample tests
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Comparing \(k\)-independent and right censored samples based on the likelihood ratio
- Non-parametric \(k\)-sample tests: density functions vs distribution functions
- Homogeneity tests based on several progressively type-II censored samples
- A test for the two-sample problem based on empirical characteristic functions
- Estimated sampling distributions: The bootstrap and competitors
- Universal Gaussian approximations under random censorship
- The central limit theorem under random censorship
- Nonparametric Estimation from Incomplete Observations
- A class of rank test procedures for censored survival data
- Two-Sample Tests of Cramér--von Mises- and Kolmogorov--Smirnov-Type for Randomly Censored Data
- Bootstrapping the Kaplan-Meier Estimator
- Censored Data and the Bootstrap
- Estimating the median survival time
- Rank tests of maximal power against Lehmann-type alternatives
- On distribution-free tests for equality of survival distributions
- Tables of the cramér-von mises distributions
- Cramér-Von Mises Statistic for Repeated Measures
- Relationships Among Tests for Censored Data
- K-Sample Analogues of the Kolmogorov-Smirnov and Cramer-V. Mises Tests
- Empirical likelihood tests for two-sample problems via nonparametric density estimation
- A generalized two-sample Wilcoxon test for doubly censored data
This page was built for publication: Testing the equality among distribution functions from independent and right censored samples via Cramér–von Mises criterion
