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Non‐parametric estimates of overlap
Author(s) -
Stine Robert A.,
Heyse Joseph F.
Publication year - 2001
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/1097-0258(20010130)20:2<215::aid-sim642>3.0.co;2-x
Subject(s) - resampling , estimator , parametric statistics , statistics , sampling (signal processing) , variance (accounting) , parametric model , mathematics , normality , nonparametric statistics , standard error , kernel (algebra) , computer science , econometrics , accounting , filter (signal processing) , combinatorics , business , computer vision
Kernel densities provide accurate non‐parametric estimates of the overlapping coefficient or the proportion of similar responses (PSR) in two populations. Non‐parametric estimates avoid strong assumptions on the shape of the populations, such as normality or equal variance, and possess sampling variation approaching that of parametric estimates. We obtain accurate standard error estimates by bootstrap resampling. We illustrate the practical use of these methods in two examples and use simulations to explore the properties of the estimators under various sampling situations. Copyright © 2001 John Wiley & Sons, Ltd.