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Variable Selection Theorem for the Analysis of Covariance Model
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 Title & Authors
Variable Selection Theorem for the Analysis of Covariance Model
Yoon, Sang-Hoo; Park, Jeong-Soo;
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 Abstract
Variable selection theorem in the linear regression model is extended to the analysis of covariance model. When some of regression variables are omitted from the model, it reduces the variance of the estimators but introduces bias. Thus an appropriate balance between a biased model and one with large variances is recommended.
 Keywords
Estimable function;generalized inverse;mean squared error;positive semi-definite matrix;reduced model;
 Language
Korean
 Cited by
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