Bayesian Credible Sets for a Binomial Proportion Based on One-Sample Binary Data Subject to One Type of Misclassification

Bayesian sample size determination for estimating binomial parameters from data subject to misclassification

We investigate the sample size problem when a binomial parameter is to be estimated, but some degree of misclassification is possible. The problem is especially challenging when the degree to which misclassification occurs is not exactly known. Motivated by a Canadian survey of the prevalence of toxoplasmosis infection in pregnant women, we examine the situation where it is desired that a margi...

One- and Two-Sample Bayesian Prediction Intervals Based on Type-I Hybrid Censored Data

One- and two-sample Bayesian prediction intervals based on progressively Type-II censored data

In this article, oneand two-sampleBayesian prediction intervals based on progressively Type-II censored data are derived. For the illustration of the developed results, the exponential, Pareto, Weibull and Burr Type-XII models are used as examples. Some of the previous results in the literature such as Dunsmore (Technometrics 16:455–460, 1974), Nigm andHamdy (Commun Stat TheoryMethods 16:1761–1...

Exact and Asymptotic Statistical Tests for Proportion Difference in One-to-Two Matched Binary Data

Matched observations with dichotomous responses commonly occur in medical and epidemiological researches. Although standard approaches exist for one-to-one paired binary data analyses, there is not much work for one-to-two or one-to-N matched binary data in the current statistical literature. The existing Miettinen’s test assumes that the multiple observations from the same matched set are mutu...

Jeffreys Interval for One-Sample Proportion with SAS/STAT Software

This paper introduces Jeffreys interval for one-sample proportion using SAS® software. It compares the credible interval from a Bayesian approach with the confidence interval from a frequentist approach. Different ways to calculate the Jeffreys interval are presented using PROC FREQ, the QUANTILE function, a SAS program of random walk Metropolis sampler, and PROC MCMC.