Truncated SPRTs with application to multivariate normal data. Issue 2 (3rd April 2017)

• Record Type: Journal Article
• Authors: Kim, Hyunkyoung; Jeske, Daniel R.
• Is Part Of: Sequential analysis. Volume 36:Issue 2(2017)
• Page Start: 251
• Page End: 277
• Publication Date: 2017-04-03
• Subjects: Backward induction -- Bayesian decision -- classifications -- disease diagnosis -- longitudinal data -- sequential hypothesis tests; 62H15 -- 62L10 -- 62C10; Sequential analysis -- Periodicals; 519.54
• DOI: 10.1080/07474946.2017.1319688
• Languages: English
• ISSNs: 0747-4946

Abstract

ABSTRACT: We develop truncated sequential probability ratio test (SPRT) procedures for multivariate normal data. The framework includes a general cost structure and arbitrary mean and covariance structures. The truncated SPRT solutions have a practical and easy-to-use decision boundary representation. In the homogeneous case, a very fast recursive algorithm is presented for calculating the decision boundaries. Misclassification rates and expected sample size are investigated and the results are compared with a nonsequential procedure. A real-life data set on kidney dysfunction following heart surgery is used to illustrate the truncated SPRT procedure.