Alternative Estimation Procedures for Pr(X < Y) in Categorized Data

DOI10.2307/2530703zbMath0613.62125OpenAlexW2327151140WikidataQ36483129 ScholiaQ36483129MaRDI QIDQ4720623

Jeffrey S. Simonoff, Yosef Hochberg, Benjamin Reiser

Publication date: 1986

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/2530703

zbMATH Keywords

robustnessconfidence intervalssimulationcontingency tablebootstrap estimate of variance

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Parametric inference (62F99) Contingency tables (62H17) Reliability and life testing (62N05)

Related Items (17)

The effect of category choice on the odds ratio and several measures of association in case-control studies ⋮ Proportional reversed hazard rate model and its applications ⋮ Smoothing categorical data ⋮ Estimation parameter of \(R=P(Y<X)\) for length-biased weighted Lomax distributions in the presence of outliers ⋮ Estimation of stress-strength reliability in the inverse Gaussian distribution under progressively type II censored data ⋮ Smoothed bootstrap confidence intervals with discrete data ⋮ Identifiability issues in dynamic stress-strength modeling ⋮ Applications of conditional power function of two-sample permutation test ⋮ Inference about reliability parameter with gamma strength and stress ⋮ Optimally and computations for relative surprise inferences ⋮ Stress-strength reliability inference for the Pareto distribution with outliers ⋮ Sample size choice for reliability verification in strength‐stress models ⋮ Interval Estimation of the Stress-Strength Reliability with Independent Normal Random Variables ⋮ Estimation of reliability in a bivariate normal distribution with equal coefficients of variation ⋮ Choice of sample size for testing the P(X>Y) ⋮ Unbiastld estimation of parametric functions in sampling from two two-truncation parameter families ⋮ Inferences for stress–strength reliability of Burr Type X distributions based on ranked set sampling

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