Weighted uniform consistency of kernel density estimators with general bandwidth sequences

Julia Dony (Free University of Brussels (VUB))
Uwe Einmahl (Free University of Brussels (VUB))

Abstract


Let $f_{n,h}$ be a kernel density estimator of a continuous and bounded $d$-dimensional density $f$. Let $\psi(t)$ be a positive continuous function such that $\|\psi f^\beta\| _\infty < \infty$ for some $0< \beta < 1/2$. We are interested in the rate of consistency of such estimators with respect to the weighted sup-norm determined by $\psi$. This problem has been considered by Gin, Koltchinskii and Zinn (2004) for a deterministic bandwidth $h_n$. We provide ``uniform in $h$'' versions of some of their results, allowing us to determine the corresponding rates of consistency for kernel density estimators where the bandwidth sequences may depend on the data and/or the location.

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Pages: 844-859

Publication Date: September 24, 2006

DOI: 10.1214/EJP.v11-354

References

  1. Deheuvels, P. (2000). Uniform limit laws for kernel density estimators on possibly unbounded intervals. Recent advances in reliability theory (Bordeaux, 2000), 477--492, Stat. Ind. Technol., Birkhuser Boston, MA. MR1783500
  2. Deheuvels, P. and Mason, D.M. (2004). General asymptotic confidence bands based on kernel-type function estimators. Stat. Inference Stoch. Process. 7(3), 225--277. MR2111291
  3. Einmahl, U. and Mason, D.M. (2000). An empirical process approach to the uniform consistency of kernel-type function estimators. J. Theoret. Probab. 13(1), 1--37. MR1744994
  4. Einmahl, U. and Mason, D.M. (2005). Uniform in bandwidth consistency of kernel-type function estimators. Ann. Statist. 33(3), 1380--1403. MR2195639
  5. Gin, E. and Guillou, A. (2002). Rates of strong uniform consistency for multivariate kernel density estimators. Ann. Inst. H. Poincar Probab. Statist. 38(6), 907--921. MR1955344
  6. Gin, E., Koltchinskii, V. and Sakhanenko, L. (2003). Convergence in distribution of self-normalized sup-norms of kernel density estimators. High dimensional probability, III (Sandjberg, 2002), Progr. Probab., 55, 241--253. Birkhuser, Basel. MR2033892
  7. Gin, E,; Koltchinskii, V. and Sakhanenko, L. (2004). Kernel density estimators: convergence in distribution for weighted sup-norms. Probab. Theory Related Fields, 130(2), 167--198 MR2093761
  8. Gin, E. Koltchinskii, V. and Zinn, J. (2004). Weighted uniform consistency of kernel density estimators. Ann. Probab. 32(3), 2570--2605. MR2078551
  9. Mason, D.M. (2003). Representations for integral functionals of kernel density estimators. Austr. J. Stat., 32(1,2), 131--142.
  10. Pollard, D. (1984). Convergence of stochastic processes. Springer Series in Statistics. Springer-Verlag, New York. MR0762984
  11. Stute, W. (1982). A law of the logarithm for kernel density estimators. Ann. Probab., 10(2), 414--422. MR0647513
  12. Stute, W. (1982). The oscillation behavior of empirical processes. Ann. Probab., 10(1), 86--107. MR0637378
  13. Stute, W. (1984). The oscillation behavior of empirical processes: the multivariate case. Ann. Probab., 12(2), 361--379. MR0735843
  14. Talagrand, M. (1994). Sharper bounds for Gaussian and empirical processes. Ann. Probab. 22(1), 28--76. MR1258865
  15. van der Vaart, A.W. and Wellner, J.A. (1996). Weak convergence and empirical processes. With applications to statistics. Springer Series in Statistics. Springer-Verlag, New York. MR1385671


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