Randomized goodness of fit tests Liese, Friedrich , 1944-2018 (VerfasserIn) Liu, Bing , 1981- (VerfasserIn) Text Text 2009 2009 eng electronic resourceremoteComputermedienOnline-Ressource1 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--(DE-627)608914215 urn:nbn:de:gbv:28-rosdok_id00005830-1 doi: 10.18453/rosdok_id00005830 oclc: 1621466464 ppn: (DE-627)198673045X