Prediction Sufficiency When the Loss Function Does Not Depend on The Unknown Parameter
Torgersen, Erik N.
Ann. Statist., Tome 5 (1977) no. 1, p. 155-163 / Harvested from Project Euclid
It is shown by Takeuchi and Akahira, 1974, that conditional independence together with a condition of "partial sufficiency" imply "prediction sufficiency" for loss functions not depending on the unknown parameter. We shall here prove that these conditions are necessary as well and thereby obtain a complete description, in terms of conditional expectations, of "prediction sufficiency" for loss functions not depending on the unknown parameter. It turns out that these conditions may be replaced by a condition of conditional independence for prior distributions.
Publié le : 1977-01-14
Classification:  Prediction sufficiency,  conditional independence for prior distributions,  minimal sufficiency,  62B05,  62C07
@article{1176343748,
     author = {Torgersen, Erik N.},
     title = {Prediction Sufficiency When the Loss Function Does Not Depend on The Unknown Parameter},
     journal = {Ann. Statist.},
     volume = {5},
     number = {1},
     year = {1977},
     pages = { 155-163},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1176343748}
}
Torgersen, Erik N. Prediction Sufficiency When the Loss Function Does Not Depend on The Unknown Parameter. Ann. Statist., Tome 5 (1977) no. 1, pp.  155-163. http://gdmltest.u-ga.fr/item/1176343748/