Journal article
Uniform convergence rates for nonparametric estimators smoothed by the beta kernel
Scandinavian journal of statistics, v 49(3), pp 1353-1382
26 Jan 2022
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This paper provides a set of uniform consistency results with rates for nonparametric density and regression estimators smoothed by the beta kernel having support on the unit interval. Weak and strong uniform convergence is explored on the basis of expanding compact sets and general sequences of smoothing parameters. The results in this paper are useful for asymptotic analysis of two-step semiparametric estimation using a first-step kernel estimate as a plug-in. We provide simulations and a real data example illustrating attractive properties of the estimators.
Metrics
Details
- Title
- Uniform convergence rates for nonparametric estimators smoothed by the beta kernel
- Creators
- Masayuki Hirukawa - Ryukoku UniversityIrina Murtazashvili - Drexel UniversityArtem Prokhorov - CIREQ
- Publication Details
- Scandinavian journal of statistics, v 49(3), pp 1353-1382
- Publisher
- Wiley
- Number of pages
- 30
- Grant note
- 19K01595 / Japan Society for the Promotion of Science; Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) 20-18-00365 / Russian Science Foundation; Russian Science Foundation (RSF)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Economics (School of Economics)
- Web of Science ID
- WOS:000746833700001
- Scopus ID
- 2-s2.0-85123595554
- Other Identifier
- 991019168139704721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Statistics & Probability