Comparing \(k\)-independent and right censored samples based on the likelihood ratio

DOI10.1007/S00180-009-0181-9zbMath1226.62012OpenAlexW1964185940MaRDI QIDQ650686

Pablo Martínez-Camblor

Publication date: 26 November 2011

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-009-0181-9

zbMATH Keywords

k-sample testsweighted tests

Mathematics Subject Classification ID

Parametric hypothesis testing (62F03) Censored data models (62N01) Monte Carlo methods (65C05)

Related Items (4)

Testing the equality among distribution functions from independent and right censored samples via Cramér–von Mises criterion ⋮ A general bootstrap algorithm for hypothesis testing ⋮ A permutation test for the two-sample right-censored model ⋮ Density comparison for independent and right censored samples via kernel smoothing

Cites Work

• Non-parametric \(k\)-sample tests: density functions vs distribution functions
• \(K\)-sample tests based on the likelihood ratio
• A test for the two-sample problem based on empirical characteristic functions
• Universal Gaussian approximations under random censorship
• Empirical likelihood based hypothesis testing
• Nonparametric Estimation from Incomplete Observations
• A class of rank test procedures for censored survival data
• Rank tests of maximal power against Lehmann-type alternatives
• On distribution-free tests for equality of survival distributions
• 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

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