Free Access
Volume 25, 2021
Page(s) 55 - 86
Published online 04 March 2021
  1. I. Abramson, On bandwidth variation in kernel estimates – a square-root law. Ann. Statist. 10 (1982) 1217–1223. [Google Scholar]
  2. H.J. Bierens, Uniform consistency of kernel estimators of a regression function under generalized conditions. J. Am. Stat. Assoc. 79 (1983) 699–707. [Google Scholar]
  3. G. Collomb, Estimation non paramétrique de la régression par la méthode du noyau: propriété de convergence asymptotiquement normale indépendante Annales de le Facultédes sciences de I’Université de Clermont. Série Mathématiques des sciences de I’Université de Clermont. Série Mathématiques 65 (1977) 24–46. [Google Scholar]
  4. U. Einmahl and D.M. Mason. An empirical process approach to the uniform consistency of kernel-type function estimators. J. Theor. Prob. 13 (2000) 1–37. [Google Scholar]
  5. U. Einmahl and D.M. Mason, Uniform in bandwidth consistency of kernel-type function estimators. Ann. Statist. 33 (2005) 1380–1403. [Google Scholar]
  6. T. Gasser and H.G. Müller, Estimating regression functions and their derivatives by the kernel method. Scand. J. Stat. 11 (1984) 171–185. [Google Scholar]
  7. E. Giné and H. Sang, Uniform asymptotics for kernel density estimators with variable bandwidths. J. Nonparametr. Stat. 22 (2010) 773–795. [Google Scholar]
  8. E. Giné and H. Sang, On the estimation of smooth densities by strict probability densities at optimal rates in sup-norm. IMS Collections, From Probability to Statistics and Back: High-Dimensional Models and Processes 9 (2013) 128–149. [Google Scholar]
  9. P. Hall, On the bias of variable bandwidth kernel estimators. Biometrika 77 (1990) 529–535. [Google Scholar]
  10. P. Hall and J.S. Marron, Variable window width kernel estimates of probability densities. Probab. Theory Related Fields 80 (1988) 37–49. Erratum: Probab. Theory Related Fields 91 (1988) 133. [Google Scholar]
  11. P. Hall, T. Hu and J.S. Marron, Improved variable window kernel estimates of probability densities. Ann. Statist. 23 (1995) 1–10. [Google Scholar]
  12. T. Hayfield and J.S. Racine, Nonparametric econometrics: The np package. J. Stat. Softw. Ann. Statist. 25 (2008) 5. [Google Scholar]
  13. M.C. Jones, I.J. McKay and T.-C. Hu, Variable location and scale kernel density estimation. Ann. Inst. Statist. Math. 46 (1994) 521–535. [Google Scholar]
  14. I.J. McKay, A note on bias reduction in variable kernel density estimates. Canad. J. Statist. 21 (1993a) 367–375. [Google Scholar]
  15. I.J. McKay. Variable kernel methods in density estimation. Ph.D Dissertation, Queen’s University, 1993b. [Google Scholar]
  16. H. Müller and U. Stadtmüller, Variable bandwidth kernel estimators of regression curves. Ann. Statist. 15 (1987) 182–201. [Google Scholar]
  17. E.A. Nadaraya. On estimating regression. Theory Prob. Appl. 9 (1964) 141–142. [Google Scholar]
  18. J. Nakarmi and H. Sang. Central limit theorem for the variable bandwidth kernel density estimators. J. Korean Stat. Soc. 47 (2018) 201–215. [Google Scholar]
  19. K. Noda, Estimation of a regression function by the Parzen kernel-type density estimators. Ann. Inst. Stat. Math. 28 (1976) 221–234. [Google Scholar]
  20. S.Y. Novak, A generalized kernel density estimator. (Russian) Teor. Veroyatnost. i Primenen. 44 (1999) 634–645; translation in Theory Probab. Appl. 44 (2000) 570–583. [Google Scholar]
  21. M. Rosenblatt, Conditional probability density and regression estimators. Multivar. Anal. II. Academic Press, New York (1969) 25–31. [Google Scholar]
  22. M.G. Schimek, Smoothing and regression: Approaches, computation, and application. John Wiley & Sons (2000). [Google Scholar]
  23. G.R. Terrell and D. Scott, Variable kernel density estimation. Ann. Statist. 20 (1992) 1236–1265. [Google Scholar]
  24. L. Wasserman, All of nonparametric statistics. Springer (2006). [Google Scholar]
  25. G.S. Watson, Smooth regression analysis. Sankhya Ser. A 26 (1964) 359–372. [Google Scholar]

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