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Data-driven efficient score tests for deconvolution hypotheses

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


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C9BB-8
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.