摘要:

Newhouse and Oman (1971) identified the orientations with respect to the eigenvectors of X'X of the true coefficient vector of the linear regression model for which the ordinary ridge regression estimator performs best and performs worse when mean squared error is the measure of performance. In this paper the corresponding result is derived for generalized ridge regression for two risk functions: mean squared error and mean squared error of prediction.

ISSN:0361-0918    

DOI:10.1080/03610919008812930

出版商:Marcel Dekker, Inc.    

年代:1990

数据来源: Taylor

摘要:

It appears to be common practice with ridge regression to obtain a decomposition of the total sum of squares, and assign degrees of freedom, according to established least squares theory. This discussion notes the obvious fallacies of such an approach, and introduces a decomposition based on orthogonality, and degrees of freedom based on expected mean squares, for non-stochastic k.

ISSN:0361-0918    

DOI:10.1080/03610919008812931

出版商:Marcel Dekker, Inc.    

年代:1990

数据来源: Taylor

摘要:

In this paper we present a modification to Newton's method that is suitable for computing percentage points as well as other parameter estimation. The method is locally quadratically convergent.

ISSN:0361-0918    

DOI:10.1080/03610919008812932

出版商:Marcel Dekker, Inc.    

年代:1990

数据来源: Taylor

摘要:

The least-squares cross-validation is a completely automatic method for choosing the smoothing parameter in probability density estimation but this method consume large amounts of computer time. This article concerns an efficient computational algorithm for this method when the kernel is symmetric and polynomial functions.

ISSN:0361-0918    

DOI:10.1080/03610919008812933

出版商:Marcel Dekker, Inc.    

年代:1990

数据来源: Taylor