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Data-driven efficient score tests for deconvolution hypotheses




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.

Author(s): Langovoy, M.
Journal: Inverse Problems
Volume: 24
Number (issue): 2
Pages: 1-17
Year: 2008
Month: April
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1088/0266-5611/24/2/025028
EPUB: 025028

Links: PDF


  title = {Data-driven efficient score tests for deconvolution hypotheses},
  author = {Langovoy, M.},
  journal = {Inverse Problems},
  volume = {24},
  number = {2},
  pages = {1-17},
  month = apr,
  year = {2008},
  month_numeric = {4}