Description Usage Arguments Details Value References Examples
View source: R/manifold1Dplus.R
Finds appropriate starting values for the 1D-algorithm.
1 | get1Dini(A,B)
|
A |
A √{n} estimate of an estimator's asymptotic covariance matrix. |
B |
A √{n} estimate of the parameter associated with the space we are enveloping. For our purposes this quantity is either the outer product of the MLE of the mean-value submodel parameter vector with itself or the outer product of the MLE of the canonical submodel parameter vector with itself. |
The 1D-algorithm is sensitive to starting values. Maximizers of
the 1D-algorithm objective function are close to eigenvectors of
A
or (A
+B
). The vector, w
, is the
eigenvector of A
or (A
+B
) which produces the
largest value of the 1D-algorithm objective function.
w |
An appropriate starting value for the 1D-algorithm. |
Cook, R.D. and Zhang, X. (2014). Foundations for Envelope Models and Methods. JASA, In Press.
Cook, R.D. and Zhang, X. (2015). Algorithms for Envelope Estimation. Journal of Computational and Graphical Statistics, Published online. doi: 10.1080/10618600.2015.1029577.
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