Showing papers on "Coverage probability published in 1980"

Estimating the Common Mean of Possibly Different Normal Populations: A Simulation Study

Abstract: Relative efficiency of estimators of the common mean of possibly different normal populations N(μ, σ i 2) is investigated empirically. A weighted least squares estimator , with weights based on a modification of minimum norm quadratic unbiased (MINQU) estimators of the σ i 2, is found to be substantially more efficient than the maximum likelihood (ML) estimator of μ when the heterogeneity in the σ i , is small to moderate and the number of sample observations from a population is small. The jackknife t statistic for performed well in regard to both coverage probability and expected length of the confidence interval.

A Sequential Confidence Interval for the Odds Ratio.

Abstract: : A sequential fixed width confidence interval is proposed for the log odds ratio of a 2 x 2 table It is shown that the proposed interval has asymptotically the correct coverage probability and is asymptotically efficient uniformly in the unknown parameters (Author)

Some remarks on conditional and unconditional inference for location-scale models

Abstract: Conditional and unconditional confidence intervals have been compared by Grice, Bain, and Engelhardt (Commun. Statist. B7 (1978), 515–524) in terms of the location-scale model with double-exponential distribution form. Preference was found for the conditional intervals based on mean length and coverage probability for untrue parameters values. These two criteria for a location-scale system are shown to be inappropriate criteria for assessing the conditional versus unconditional approaches to inference. The usual ancillarity concept is also noted to be inappropriate. Support for many conditional analyses, however, is found in a more careful formulation of the statistical model.

On some coverage probability maximization problems

Abstract: Duals of bilinear programming problems are used to establish some optimality properties of probability coverage optimization problems. These problems include confidence interval problems and hypothesis testing problems in particular cases.

Confidence Intervals for Comparing Two Life Distributions

Abstract: The performance of several s-confidence interval procedures for comparing two life distributions is studied by Monte Carlo methods. Three life models are considered: exponentials, nonparametric proportional hazard functions (Lehmann alternatives), and nonparametric scale alternatives. The s-confidence procedures are based on a) for exponential distributions: The F-statistic, a likelihood ratio statistic (LR), and maximum likelihood estimator (MLE), b) for nonparametric proportional hazard functions, a LR and MLE based on Cox's conditional likelihood function, c) for nonparametric scale alternatives, the generalized Wilcoxon and the Cox-Mantel statistics. The procedures are compared with respect to coverage probabilities, robustness, and power. The simulations include several cases of censored and uncensored samples from the Weibull distribution. When samples are from exponential distributions, with or without censoring, all the procedures are valid. The three parametric procedures have higher power than the nonparametric procedures when there is no censoring and have similar power when there is censoring. When samples are from the Weibull distributions, the three parametric procedures are not robust. If the two shape parameters are equal, the procedures for scale alternative models and for proportional hazard models are valid. If the shape parameters are not equal, none of the procedures are appropriate and some more complicated method should be used.