Asymptotic normality of a consistent estimator of maximum mean discrepancy in Hilbert space

2020 ◽  
Vol 156 ◽  
pp. 108596 ◽  
Author(s):  
Natsumi Makigusa ◽  
Kanta Naito
2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Alfredas Račkauskas

Abstract We investigate the asymptotic normality of distributions of the sequence {\sum_{k\in\mathbb{Z}}u_{n,k}X_{k}} , {n\in\mathbb{N}} , where {(X_{k},k\in\mathbb{Z})} either is a sequence of i.i.d. random elements or constitutes a linear process with i.i.d. innovations in a separable Hilbert space. The weights {(u_{n,k})} are in general a family of linear bounded operators. This model includes operator weighted sums of Hilbert space valued linear processes, operator-wise discounted sums in a Hilbert space as well some extensions of classical summation methods.


2015 ◽  
Vol 22 (2) ◽  
Author(s):  
Petre Babilua ◽  
Elizbar Nadaraya ◽  
Grigol Sokhadze

AbstractWe develop the method of maximal likelihood for infinite-dimensional Hilbert spaces and prove several theorems about consistency and asymptotic normality.


2021 ◽  
Vol 169 ◽  
pp. 108961
Author(s):  
Armando Sosthène Kali Balogoun ◽  
Guy Martial Nkiet ◽  
Carlos Ogouyandjou

Author(s):  
J. R. Retherford
Keyword(s):  

1998 ◽  
Vol 14 (4) ◽  
pp. 833-848
Author(s):  
Malcolm P. Quine ◽  
Władysław Szczotka
Keyword(s):  

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