Name
Abstract | ||
|---|---|---|
Mallows' C"p statistic is widely used for selecting multivariate linear regression models. It can be considered to be an estimator of a risk function based on an expected standardized mean square error of prediction. An unbiased C"p criterion for selecting multivariate linear regression models has been proposed. In this paper, that unbiased C"p criterion is extended to the case of a multivariate ridge regression. It is analytically proved that the proposed criterion has not only a smaller bias but also a smaller variance than the existing C"p criterion, and is the uniformly minimum variance unbiased estimator of the risk function. We show that the criterion has useful properties by means of numerical experiments. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.jmva.2009.09.017 | J. Multivariate Analysis |
Keywords | Field | DocType |
ridge regression,multivariate ridge regression,bias correction,secondary,smaller bias,unbiased c,62f07,unbiased cp criterion,mallows’ c p statistic,minimum variance unbiased estimator,p criterion,multivariate linear regression model,primary,62j07,existing c,p statistic,risk function,proposed criterion,model selection,statistical model,multivariate linear regression | Best linear unbiased prediction,Econometrics,Minimum-variance unbiased estimator,Bayesian information criterion,Stepwise regression,Stein's unbiased risk estimate,Mean squared error,Bayesian multivariate linear regression,Statistics,Mathematics,Linear regression | Journal |
Volume | Issue | ISSN |
101 | 5 | Journal of Multivariate Analysis |
Citations | PageRank | References |
4 | 1.02 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hirokazu Yanagihara | 1 | 21 | 8.66 |
| Kenichi Satoh | 2 | 19 | 2.23 |