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Distribution-free consistency of a nonparametric kernel regression estimate and classification

✍ Scribed by Krzyzak, A.; Pawlak, M.


Book ID
114635355
Publisher
IEEE
Year
1984
Tongue
English
Weight
488 KB
Volume
30
Category
Article
ISSN
0018-9448

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πŸ“œ SIMILAR VOLUMES


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Under quite mild conditions on the kernel and on the bandwidth, the distribution-free strong consistency is proved for the kernel regression and the modified kernel regression of an ~-miΓ—in~ stationary sequence in time series context. The condition imposed on the mixing coefficients ..... . l,, \~=~

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We prove that in the case of independent and identically distributed random vectors (X i ; Y i ) a class of kernel type M-estimators is universally and strongly consistent for conditional M-functionals. The term universal means that the strong consistency holds for all joint probability distribution