Weak Convergence of the Sample Distribution Function when Parameters are Estimated
Durbin, J.
Ann. Statist., Tome 1 (1973) no. 2, p. 279-290 / Harvested from Project Euclid
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The weak convergence of the sample df is studied under a given sequence of alternative hypotheses when parameters are estimated from the data. For a general class of estimators it is shown that the sample df, when normalised, converges weakly to a specified normal process. The results are specialised to the case of efficient estimation.
Publié le : 1973-03-14
Classification:  
@article{1176342365,
     author = {Durbin, J.},
     title = {Weak Convergence of the Sample Distribution Function when Parameters are Estimated},
     journal = {Ann. Statist.},
     volume = {1},
     number = {2},
     year = {1973},
     pages = { 279-290},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1176342365}
}

Durbin, J. Weak Convergence of the Sample Distribution Function when Parameters are Estimated. Ann. Statist., Tome 1 (1973) no. 2, pp.  279-290. http://gdmltest.u-ga.fr/item/1176342365/