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  Data-driven efficient score tests for deconvolution hypotheses

Langovoy, M. (2008). Data-driven efficient score tests for deconvolution hypotheses. Inverse Problems, 24(2), 1-17. doi:10.1088/0266-5611/24/2/025028.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C9BB-8 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C9BC-6
Genre: Journal Article

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Langovoy, M1, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: We consider testing statistical hypotheses about densities of signals in deconvolution models. A new approach to this problem is proposed. We constructed score tests for the deconvolution density testing with the known noise density and efficient score tests for the case of unknown density. The tests are incorporated with model selection rules to choose reasonable model dimensions automatically by the data. Consistency of the tests is proved.

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 Dates: 2008-04
 Publication Status: Issued
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 Identifiers: URI: http://iopscience.iop.org/0266-5611/24/2/025028/pdf/0266-5611_24_2_025028.pdf
DOI: 10.1088/0266-5611/24/2/025028
BibTex Citekey: Langovoy2008
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Title: Inverse Problems
Source Genre: Journal
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Pages: - Volume / Issue: 24 (2) Sequence Number: - Start / End Page: 1 - 17 Identifier: -