Reduced Rank Regression With Applications to Quantitative Structure-Activity Relationships
Reduced rank regression is widely used in statistics to model multivariate data. In this monograph, theoretical and data analytical approaches are developed for the application of reduced rank regression in multivariate prediction problems. For the first time, both classical and Bayesian inference i...
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| Format: | eBook |
| Language: | English |
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Heidelberg
Physica
1995, 1995
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| Edition: | 1st ed. 1995 |
| Series: | Contributions to Statistics
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
| Summary: | Reduced rank regression is widely used in statistics to model multivariate data. In this monograph, theoretical and data analytical approaches are developed for the application of reduced rank regression in multivariate prediction problems. For the first time, both classical and Bayesian inference is discussed, using recently proposed procedures such as the ECM-algorithm and the Gibbs sampler. All methods are motivated and illustrated by examples taken from the area of quantitative structure-activity relationships (QSAR) |
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| Physical Description: | X, 179 p online resource |
| ISBN: | 9783642500152 |