Data-driven efficient score tests for deconvolution hypotheses

M. Langovoy

    Research output: Contribution to journalArticleAcademicpeer-review

    1 Citation (Scopus)

    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.
    Original languageEnglish
    Pages (from-to)025028-1/17
    Number of pages18
    JournalInverse Problems
    Volume24
    Issue number2
    DOIs
    Publication statusPublished - 2008

    Fingerprint Dive into the research topics of 'Data-driven efficient score tests for deconvolution hypotheses'. Together they form a unique fingerprint.

    Cite this