Open Access
Issue
Math. Model. Nat. Phenom.
Volume 20, 2025
Article Number 9
Number of page(s) 12
Section Mathematical methods
DOI https://doi.org/10.1051/mmnp/2025008
Published online 04 April 2025
  1. O. Eidous and S. Al-Talafha, Kernel method for overlapping coefficients estimation. Commun. Statist. Simul. Computat. 51 (2022) 5139–5156. [Google Scholar]
  2. M.S. Weitzman, Measures of overlap of income distributions of white and negro families in the United States. Technical Report 22, US Department of Commerce (1970). [Google Scholar]
  3. Q. Xu, X. Wang, J. Yi and Y. Wang, Bias correction in species distribution models based on geographic and environmental characteristics. Ecol. Inform. 61 (2024) 1–14. [Google Scholar]
  4. H.F. Inman and E.L. Bradley Jr, The overlapping coefficient as a measure of agreement between probability distributions and point estimation of the overlap of two normal densities. Commun. Statist. Theory Methods 18 (1989) 3851–3874. [Google Scholar]
  5. J. Wolff, G. Hefner, C. Normann, K. Kaier, H. Binder, C. Hiemke, S. Toto, K. Domschchke, M. Marschollek and A. Klimke, Polypharmacy and the risk of drug-drug interactions and potentially inappropriate medications in hospital psychiatry. Pharmacoepidemiol. Drug Saf. 30 (2021) 1258–1268. [Google Scholar]
  6. S.P. Jenkins and P. Van Kerm, The Measurement of Economic Inequality. The Oxford Handbook of Economic Inequality (2012). https://doi.org/10.1093/oxfordhb/9780199606061.013.0003 [Google Scholar]
  7. S. Mizuno, T. Yamaguchi, A. Fukushima, Y. Matsuyama and Y. Ohashi, Overlap coefficient for assessing the similarity of pharmacokinetic data between ethnically different populations. Clin. Trials 2 (2005) 174–181. [Google Scholar]
  8. F. Schmid and A. Schmidt, Nonparametric estimation of the coefficient of overlapping—theory and empirical application. Computai. Statist. Data Anal. 50 (2006) 1583–1596. [Google Scholar]
  9. J.A. Ekstrom and G.T. Lau, Exploratory text mining of ocean law to measure overlapping agency and jurisdictional authority. Proceedings of the 2008 international conference on Digital government research, 53–62. [Google Scholar]
  10. M.S. Ridout and M. Linkie, Estimating overlap of daily activity patterns from camera trap data. J. Agric. Biol. Environ. Statist. 14 (2009) 322–337. [Google Scholar]
  11. T. Alodat, M. Al Fayez and O. Eidous, On the asymptotic distribution of Matusita’s overlapping measure. Commun. Statist. Theory Methods 51 (2022) 6963–6977. [Google Scholar]
  12. G. Núñez-Antonio, M. Mendoza, A. Contreras-Cristan, E. Gutierrez-Pena and E. Mendoza, Bayesian nonparametric inference for the overlap of daily animal activity patterns. Environ. Ecol. Statist. 25 (2018) 471–494. [Google Scholar]
  13. B. Reiser and D. Faraggi, Confidence intervals for the overlapping coefficient: the normal equal variance case. J. Roy. Statist. Soc. Ser. D (tatistician) 48 (1999) 413–418. [Google Scholar]
  14. M.S. Mulekar and S.N. Mishra, Overlap coefficient of two normal densities: equal means case. J. Japan Statist. Soc. 24 (1994) 169–180 (Japanese issue). [Google Scholar]
  15. M.S. Mulekar and S.N. Mishra, Confidence interval estimation of overlap: equal means case. Computat. Statist. Data Anal. 34 (2000) 121–137. [Google Scholar]
  16. D. Wang and L. Tian, Parametric methods for confidence interval estimation of overlap coefficients. Computat. Statist. Data Anal. 106 (2017) 12–26. [Google Scholar]
  17. O. Eidous and A. Alshorman, Estimating the overlapping coefficient in the case of normal distributions. World J. Math. 1 (2023) 1–13. [Google Scholar]
  18. O. Eidous and S. Daradkeh, On inference of Weitzman overlapping coefficient Д(Х, Y) in the case for two normal distributions. Int. J. Theor. Appl. Math. 10 (2024) 14–22. [Google Scholar]
  19. O. Al-Saidy, H.M. Samawi and M.F. Al-Saleh, Inference on overlap coefficients under the Weibull distribution: equal shape parameter. ESAIM: Probab. Statist. 9 (2005) 206–219. [Google Scholar]
  20. O. Eidous and M. Abu Al-Hayja’a, Numerical integration approximations to estimate the Weitzman overlapping measure: Weibull distributions. Yugoslav J. Oper. Res. 33 (2023) 699–712. [Google Scholar]
  21. Y.P. Chaubey, D. Sen and S.N. Mishra, Inference on overlap for two inverse Gaussian populations: equal means case. Commun. Statist. Theory Methods 37 (2008) 1880–1894. [Google Scholar]
  22. J.A. Montoya, G.P. Figueroa and D. Gonzúalez-Súanchez, Statistical inference for the Weitzman overlapping coefficient in a family of distributions. Appl. Math. Model. 71 (2019) 558–568. [Google Scholar]
  23. H. Dhaker, E. Deme and S. El-Adlouni, On inference of overlapping coefficients in two inverse Lomax populations. Statist. Theory Appl. (2021) 61–75. [Google Scholar]
  24. V. Inúacio and J.E.G. Guillúen, Bayesian nonparametric inference for the overlap coefficient: with an application to disease diagnosis. Statist. Med. 41 (2022) 3879–3898. [Google Scholar]
  25. T.E. Clemons, Erratum to “A nonparametric measure of the overlapping coefficient”. Computat. Statist. Data Anal. 36 (2001) 243. [Google Scholar]
  26. O. Eidous and E. Ananbeh, Kernel method for estimating matusita overlapping coefficient using numerical approximations. Ann. Data Sci. (2024) 1–19. [Google Scholar]
  27. O. Eidous and E. Ananbeh, Kernel method for estimating overlapping coefficient using numerical integration methods. Appl. Math. Computat. 462 (2024) 1–10. [Google Scholar]
  28. O. Eidous, Bias correction for histogram estimator using line transect sampling. Environmetrics 16 (2005) 61–69. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.