Theodore von Kármán Fellow - Seminar

 

Freitag, 12.02.2016, 10.30 Uhr

Good Confidence Intervals for Categorical Data Analyses

Alan Agresti, Distinguished Professor Emeritus, Department of Statistics, University of Florida, USA

Abstract:This talk surveys confidence intervals that perform well for estimating parameters used in categorical data analysis. Considerable research has now shown that intervals resulting from inverting score tests perform much better than inverting Wald tests and often better than inverting likelihood-ratio tests. For small samples, `exact' methods are conservative inferentially, but inverting a score test using the mid-P value provides a sensible
compromise. We also briefly review an effective pseudo-score approach that approximates the score interval for proportions and their differences with independent or dependent samples by adding pseudo data before forming simple Wald confidence intervals.

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Freitag, 12.02.2016, 10:30 - 11:30 Uhr
Forum 8 (Karman-Auditorium)