%0 Journal Article %1 shibata_optimal_1981 %A Shibata, Ritei %D 1981 %J Biometrika %K imported %N 1 %P 45--54 %R 10.1093/biomet/68.1.45 %T An optimal selection of regression variables %U http://biomet.oxfordjournals.org/content/68/1/45 %V 68 %X An asymptotically optimal selection of regression variables is proposed. The key assumption is that the number of control variables is infinite or increases with the sample size. It is also shown that Mallows's Cp', Akaike's FPE and aic methods are all asymptotically equivalent to this method.

@article{shibata_optimal_1981, abstract = {An asymptotically optimal selection of regression variables is proposed. The key assumption is that the number of control variables is infinite or increases with the sample size. It is also shown that Mallows's Cp', Akaike's FPE and aic methods are all asymptotically equivalent to this method.}, added-at = {2017-01-09T13:57:26.000+0100}, author = {Shibata, Ritei}, biburl = {https://www.bibsonomy.org/bibtex/29999be3716fd24a18b4926d41a1f4f83/yourwelcome}, doi = {10.1093/biomet/68.1.45}, interhash = {5fb05476c7284136faf9c302f580256c}, intrahash = {9999be3716fd24a18b4926d41a1f4f83}, issn = {0006-3444, 1464-3510}, journal = {Biometrika}, keywords = {imported}, language = {en}, month = apr, number = 1, pages = {45--54}, timestamp = {2017-01-09T14:01:11.000+0100}, title = {An optimal selection of regression variables}, url = {http://biomet.oxfordjournals.org/content/68/1/45}, urldate = {2016-06-28}, volume = 68, year = 1981 }