title: Non-parametric estimates of overlap.
abstract: 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.
authors.affiliation: Department of Statistics, University of Pennsylvania, Philadelphia, PA 19104, USA.
authors.forename: R A
authors.initials: RA
authors.lastname: Stine
authors.forename: J F
authors.initials: JF
authors.lastname: Heyse
mesh_terms: D000085:Acetates / Q000627:therapeutic use; D018927:Anti-Asthmatic Agents / Q000627:therapeutic use; D001249:Asthma / Q000188:drug therapy; D001507:Beclomethasone / Q000627:therapeutic use; D003198:Computer Simulation; D003521:Cyclopropanes; D003627:Data Interpretation, Statistical; D005260:Female; D006615:Hip / Q000473:pathology; D006801:Humans; D008954:Models, Biological; D015663:Osteoporosis, Postmenopausal / Q000473:pathology; D011804:Quinolines / Q000627:therapeutic use; D018709:Statistics, Nonparametric; D013440:Sulfides
chemical_list: D000085:Acetates; D018927:Anti-Asthmatic Agents; D003521:Cyclopropanes; D011804:Quinolines; D013440:Sulfides; D001507:Beclomethasone; C093875:montelukast