Free Access
Issue |
ESAIM: PS
Volume 25, 2021
|
|
---|---|---|
Page(s) | 55 - 86 | |
DOI | https://doi.org/10.1051/ps/2021003 | |
Published online | 04 March 2021 |
- I. Abramson, On bandwidth variation in kernel estimates – a square-root law. Ann. Statist. 10 (1982) 1217–1223. [Google Scholar]
- 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]
- 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]
- 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]
- U. Einmahl and D.M. Mason, Uniform in bandwidth consistency of kernel-type function estimators. Ann. Statist. 33 (2005) 1380–1403. [Google Scholar]
- 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]
- E. Giné and H. Sang, Uniform asymptotics for kernel density estimators with variable bandwidths. J. Nonparametr. Stat. 22 (2010) 773–795. [Google Scholar]
- 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]
- P. Hall, On the bias of variable bandwidth kernel estimators. Biometrika 77 (1990) 529–535. [Google Scholar]
- 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]
- P. Hall, T. Hu and J.S. Marron, Improved variable window kernel estimates of probability densities. Ann. Statist. 23 (1995) 1–10. [Google Scholar]
- T. Hayfield and J.S. Racine, Nonparametric econometrics: The np package. J. Stat. Softw. Ann. Statist. 25 (2008) 5. [Google Scholar]
- 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]
- I.J. McKay, A note on bias reduction in variable kernel density estimates. Canad. J. Statist. 21 (1993a) 367–375. [Google Scholar]
- I.J. McKay. Variable kernel methods in density estimation. Ph.D Dissertation, Queen’s University, 1993b. [Google Scholar]
- H. Müller and U. Stadtmüller, Variable bandwidth kernel estimators of regression curves. Ann. Statist. 15 (1987) 182–201. [Google Scholar]
- E.A. Nadaraya. On estimating regression. Theory Prob. Appl. 9 (1964) 141–142. [Google Scholar]
- 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]
- K. Noda, Estimation of a regression function by the Parzen kernel-type density estimators. Ann. Inst. Stat. Math. 28 (1976) 221–234. [Google Scholar]
- 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]
- M. Rosenblatt, Conditional probability density and regression estimators. Multivar. Anal. II. Academic Press, New York (1969) 25–31. [Google Scholar]
- M.G. Schimek, Smoothing and regression: Approaches, computation, and application. John Wiley & Sons (2000). [Google Scholar]
- G.R. Terrell and D. Scott, Variable kernel density estimation. Ann. Statist. 20 (1992) 1236–1265. [Google Scholar]
- L. Wasserman, All of nonparametric statistics. Springer (2006). [Google Scholar]
- G.S. Watson, Smooth regression analysis. Sankhya Ser. A 26 (1964) 359–372. [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.