Randomized goodness of fit tests Liese, Friedrich 1944-2018 VerfasserIn aut Liu, Bing 1981- VerfasserIn aut text Text gw 2009 monographic XA-DE Rostock University of Rostock, Institute of Mathematics publisher 2009 Rostock Universitätsbibliothek distributor eng
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1 Online-Ressource (28 Seiten) Goodness of fit tests based on the empirical distribution function fail to be asymptotically distribution free if parameters are estimated. Asymptotically, the empirical process with estimated parameters is a centered Gaussian process with a covariance that di¤ers from the covariance function of the Brownian bridge. If the maximum likelihood estimator is used then the new covariance function is smaller, in the Loewner semiorder, than the covariance function of the Brownian bridge. Therefore one may transform the empirical process with estimated parameters back to a Brownian bridge by adding an independent process that is suitably constructed. Classical goodness of …t statistics have aftzer this transformation an asymptotic distribution as in the case of known parameters. The power under local alternatives of this new goodness of fit tests is studied and computer simulations compare the new test with their bootstrap versions.<eng> Open Access Urheberrechtsschutz 1.0 F. Liese and B. Liu 510 https://purl.uni-rostock.de/rosdok/id00005830 Randomized Goodness of Fit Tests Rostock, 2009
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 Druck-Ausgabe (DE-627)608914215 Liese, Friedrich, 1944 - 2018 Preprints aus dem Institut für Mathematik 09/1 urn:nbn:de:gbv:28-rosdok_id00005830-1 10.18453/rosdok_id00005830 1621466464 rda DE-627 261014 198673045X 20261014T132122.0 Converted from MARCXML to MODS version 3.8 using MARC21slim2MODS3-8_XSLT1-0.xsl (Revision 1.174 20250328) ger