A nonparametric measure of the overlapping coefficient.
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Publication:1589456
DOI10.1016/S0167-9473(99)00074-2zbMath1052.62514OpenAlexW2065506833WikidataQ127560278 ScholiaQ127560278MaRDI QIDQ1589456
T. E. Clemons, Edwin L. jun. Bradley
Publication date: 12 December 2000
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/s0167-9473(99)00074-2
Related Items (14)
Kernel method for overlapping coefficients estimation ⋮ Parametric methods for confidence interval estimation of overlap coefficients ⋮ Testing the equality of two double-parameter exponential distributions via overlap coefficient ⋮ Bayesian analysis in the \(L^{1}\)-norm of the mixing proportion using discriminant analysis ⋮ A nonparametric test of symmetry based on the overlapping coefficient ⋮ The Effectiveness of Multistage Ranked Set Sampling in Stratifying the Population ⋮ Czekanowski's Index of Overlap, itsLp-Type Extension, and BIAS Reduction ⋮ Nonparametric estimation of the coefficient of overlapping-theory and empirical application ⋮ Nonparametric estimation and inference about the overlap of two distributions ⋮ Power maps in goodness-of-fit testing ⋮ Estimating overlap of daily activity patterns from camera trap data ⋮ On the Similarity Structure of Order Statistics ⋮ Inference on overlap coefficients under the Weibull distribution: Equal shape parameter ⋮ On inference of overlapping coefficients in two \textit{Lomax} populations using different sampling methods
Uses Software
Cites Work
- Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy
- A practical guide to splines
- Bootstrap methods: another look at the jackknife
- Remarks on Some Nonparametric Estimates of a Density Function
- The overlapping coefficient as a measure of agreement between probability distributions and point estimation of the overlap of two normal densities
- Nonparametric estimates of standard error: The jackknife, the bootstrap and other methods
- Estimation of probability densities by empirical density functions†
- Nonparametric Probability Density Estimation: I. A Summary of Available Methods
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