EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 19 (2015) ESAIM: PS, 19 (2015) 28-59 References
Free Access
Issue
ESAIM: PS
Volume 19, 2015
Page(s) 28 - 59
DOI https://doi.org/10.1051/ps/2014011
Published online 01 May 2015
  1. J. Beirlant, Y. Goegebeur, J. Segers and J. Teugels, Statistics of extremes: theory and applications. John Wiley & Sons Inc (2004). [Google Scholar]
  2. Y.G. Berger, Rate of convergence to normal distribution for the Horvitz−Thompson estimator. J. Stat. Plann. Inference 67 (1998) 209–226. [CrossRef] [Google Scholar]
  3. P. Bertail, E. Chautru and S. Clémençon, Empirical processes in survey sampling. Submitted (2013). [Google Scholar]
  4. N.H. Bingham, C.M. Goldie and J.L. Teugels, Regular variation. Encycl. Math. Appl. Cambridge Univ Press, Cambridge (1987). [Google Scholar]
  5. D. Bonnéry, J. Breidt and F. Coquet, Propriétés asymptotiques de l’échantillon dans le cas d’un plan de sondage informatif. Submitted (2011). [Google Scholar]
  6. N.E. Breslow, T. Lumley, C. Ballantyne, L. Chambless and M. Kulich, Improved Horvitz−Thompson estimation of model parameters from two-phase stratified samples: applications in epidemiology. Stat. Biosci. 1 (2009) 32–49. [CrossRef] [PubMed] [Google Scholar]
  7. N.E. Breslow and J.A. Wellner, Weighted likelihood for semiparametric models and two-phase stratified samples, with application to Cox regression. Scand. J. Stat. 35 (2007) 186–192. [CrossRef] [Google Scholar]
  8. N.E. Breslow and J.A. Wellner, A Z-theorem with estimated nuisance parameters and correction note for “Weighted likelihood for semiparametric models and two-phase stratified samples, with application to Cox regression”. Scand. J. Stat. 35 (2008) 186–192. [Google Scholar]
  9. W.G. Cochran, Sampling techniques. Wiley, New York (1977). [Google Scholar]
  10. J. Danielsson, L. De Haan, L. Peng and C.G. De Vries, Using a bootstrap method to choose the sample fraction in tail index estimation. J. Multivariate Anal. 76 (2001) 226–248. [CrossRef] [MathSciNet] [Google Scholar]
  11. L. De Haan and A. Ferreira, Extreme value theory: an introduction. Springer Verlag (2006). [Google Scholar]
  12. L. de Haan and L. Peng. Comparison of tail index estimators. Stat. Neerl. 52 (1998) 60–70. [CrossRef] [Google Scholar]
  13. L. de Haan and S. Resnick, On asymptotic normality of the Hill estimator. Stoch. Models 14 (1998) 849–867. [CrossRef] [Google Scholar]
  14. L. de Haan and S. Stadtmüller, Generalized regular variation of second order. J. Austral. Math. Soc. Ser. A 61 (1996) 381–295. [CrossRef] [MathSciNet] [Google Scholar]
  15. J.C. Deville, Réplications d’échantillons, demi-échantillons, Jackknife, bootstrap dans les sondages. Economica, Ed. Droesbeke, Tassi, Fichet (1987). [Google Scholar]
  16. J.C. Deville and C.E. Särndal, Calibration estimators in survey sampling. J. Am. Stat. Assoc. 87 (1992) 376–382. [CrossRef] [Google Scholar]
  17. W. Feller, An introduction to probability theory and its applications, 2nd edition. John Wiley & Sons Inc., New York (1971). [Google Scholar]
  18. R.D. Gill, Y. Vardi and J.A. Wellner, Large sample theory of empirical distributions in biased sampling models. Ann. Stat. 16 (1988) 1069–1112. [CrossRef] [Google Scholar]
  19. Y. Goegebeur, J. Beirlant and T. de Wet, Linking Pareto-tail kernel goodness-of-fit statistics with tail index at optimal threshold and second order estimation. Revstat 6 (2008) 51–69. [MathSciNet] [Google Scholar]
  20. C.M. Goldie and R.L. Smith, Slow variation with remainder: theory and applications. Quart. J. Math. Oxford 38 (1987) 45–71. [CrossRef] [Google Scholar]
  21. M.I. Gomes and O. Oliveira, The bootstrap methodology in statistics of extremes – choice of the optimal sample fraction. Extremes 4 (2001) 331–358. [CrossRef] [Google Scholar]
  22. C. Gourieroux, Théorie des sondages. Economica (1981). [Google Scholar]
  23. C. Gourieroux, Effets d’un sondage: cas du χ2 et de la régression. Economica, Ed. Droesbeke, Tassi, Fichet (1987). [Google Scholar]
  24. J. Hajek, Asymptotic theory of rejective sampling with varying probabilities from a finite population. Ann. Math. Stat. 35 (1964) 1491–1523. [CrossRef] [Google Scholar]
  25. H.O. Hartley and J.N.K. Rao, Sampling with unequal probabilities and without replacement. Ann. Math. Stat. 33 (1962) 350–374. [CrossRef] [Google Scholar]
  26. B.M. Hill, A simple general approach to inference about the tail of a distribution. Ann. Stat. 3 (1975) 1163–1174. [Google Scholar]
  27. D.G. Horvitz and D.J. Thompson, A generalization of sampling without replacement from a finite universe. J. Am. Stat. Assoc. 47 (1951) 663–685. [CrossRef] [Google Scholar]
  28. D.M. Mason, Laws of large numbers for sums of extreme values. Ann. Probab. 10 (1982) 756–764. [Google Scholar]
  29. R.B. Nelsen, An introduction to copulas. Springer (1999). [Google Scholar]
  30. S.I. Resnick, Heavy-tail phenomena: probabilistic and statistical modeling. Springer Verlag (2007). [Google Scholar]
  31. P.M. Robinson, On the convergence of the Horvitz−Thompson estimator. Austral. J. Stat. 24 (1982) 234–238. [CrossRef] [Google Scholar]
  32. P. Rosen, Asymptotic theory for successive sampling. J. Am. Math. Soc. 43 (1972) 373–397. [Google Scholar]
  33. T. Saegusa and J.A. Wellner, Weighted likelihood estimation under two-phase sampling. Preprint available at http://arxiv.org/abs/1112.4951v1 (2011). [Google Scholar]
  34. Y. Tillé, Sampling algorithms. Springer Ser. Stat. (2006). [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.