Multiscale inference for multivariate deconvolution | Zendy

Konstantin Eckle | Zendy; Nicolai Bissantz | Zendy; Holger Dette | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Multiscale inference for multivariate deconvolution

Author(s) -

Konstantin Eckle,

Nicolai Bissantz,

Holger Dette

Publication year - 2017

Publication title -

electronic journal of statistics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.482

H-Index - 54

ISSN - 1935-7524

DOI - 10.1214/17-ejs1355

Subject(s) - deconvolution , multivariate statistics , mathematics , inference , monotonic function , point (geometry) , algorithm , statistical inference , multivariate kernel density estimation , point process , density estimation , artificial intelligence , statistics , computer science , mathematical analysis , geometry , estimator , kernel method , variable kernel density estimation , support vector machine

In this paper we provide new methodology for inference of the geometric features of a multivariate density in deconvolution. Our approach is based on multiscale tests to detect significant directional derivatives of the unknown density at arbitrary points in arbitrary directions. The multiscale method is used to identify regions of monotonicity and to construct a general procedure for the detection of modes of the multivariate density. Moreover, as an important application a significance test for the presence of a local maximum at a pre-specified point is proposed. The performance of the new methods is investigated from a theoretical point of view and the finite sample properties are illustrated by means of a small simulation study.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research