A Functional Central Limit Theorem for Kernel Type Density Estimators | Zendy

István Fazekas | Zendy; Peter Filzmoser | Zendy

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A Functional Central Limit Theorem for Kernel Type Density Estimators

Author(s) -

István Fazekas,

Peter Filzmoser

Publication year - 2016

Publication title -

austrian journal of statistics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.342

H-Index - 9

ISSN - 1026-597X

DOI - 10.17713/ajs.v35i4.351

Subject(s) - central limit theorem , mathematics , kernel (algebra) , estimator , limit (mathematics) , type (biology) , kernel density estimation , multivariate kernel density estimation , square integrable function , sequence (biology) , uniform limit theorem , mathematical analysis , pure mathematics , kernel method , variable kernel density estimation , statistics , computer science , ecology , genetics , artificial intelligence , support vector machine , biology

Kernel type density estimators are studied for random fields. A functional central limit theorem in the space of square integrable functions is proved if the locations of observations become more and more dense in an increasing sequence of domains.

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