Some Asymptotic Theory for Functional Regression and Classification | Zendy

F.H. Ruymgaart | Zendy; Jing Wang | Zendy; ShihHsuan Wei | Zendy; Yu Li | Zendy

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Some Asymptotic Theory for Functional Regression and Classification

Author(s) -

F.H. Ruymgaart,

Jing Wang,

ShihHsuan Wei,

Yu Li

Publication year - 2011

Publication title -

advances in decision sciences

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.178

H-Index - 13

eISSN - 2090-3367

pISSN - 2090-3359

DOI - 10.1155/2011/485974

Subject(s) - estimator , mathematics , asymptotic analysis , simple (philosophy) , gaussian , asymptotic distribution , regression , regression analysis , linear regression , asymptotic expansion , mathematical optimization , statistics , mathematical analysis , philosophy , physics , epistemology , quantum mechanics

Exploiting an expansion for analytic functions of operators, the asymptotic distribution of an estimator of the functional regression parameter is obtained in a rather simple way; the result is applied to testing linear hypotheses. The expansion is also used to obtain a quick proof for the asymptotic optimality of a functional classification rule, given Gaussian populations

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