Abstract
I address the issue of ill-conditioned regressors within generalized estimating equations (GEEs). In such a setting, standard GEE approaches can have problems with: convergence, large coefficient variances, poor prediction, deflated power of tests, and in some extreme cases, e.g. functional regressors, may not even exist. I modify the quasi-likelihood score functions, while presenting a variety of biased estimators that simultaneously address the issues of (severe) ill-conditioning and correlated response variables. To simplify the presentation, I attempt to unite or link these estimators as much as possible. Some properties, as well as some guidelines for choosing the meta or penalty parameters are suggested.
Item Type: | Paper |
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Faculties: | Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386 Special Research Fields > Special Research Field 386 |
Subjects: | 500 Science > 510 Mathematics |
URN: | urn:nbn:de:bvb:19-epub-1572-2 |
Language: | English |
Item ID: | 1572 |
Date Deposited: | 05. Apr 2007 |
Last Modified: | 04. Nov 2020, 12:45 |