Confidence Intervals for a Proportion Using Inverse Sampling when the Data is Subject to False-positive Misclassification

Journal of Data Science

article

Kent Riggs¹ 

[1] Department of Mathematics and Statistics, Stephen F. Austin State University

关键词: Misclassification;    Double sampling;    Inverse sampling;   

DOI  :  10.6339/JDS.201510_13(4).0001

学科分类：土木及结构工程学

来源: JDS

【 摘 要 】

Of interest in this paper is the development of a model that uses inverse sampling of binary data that is subject to false-positive misclassification in an effort to estimate a proportion. From this model, both the proportion of success and false positive misclassification rate may be estimated. Also, three first-order likelihood based confidence intervals for the proportion of success are mathematically derived and studied via a Monte Carlo simulation. The simulation results indicate that the score and likelihood ratio intervals are generally preferable over the Wald interval. Lastly, the model is applied to a medical data set.

【 授权许可】

CC BY