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).
This handbook in two volumes synthesises our knowledge about the ecology of Central Europe’s plant cover with its 7000-yr history of human impact, covering Germany, Poland, the Netherlands, Belgium, Luxembourg, Switzerland, Austria, Czech Republic and Slovakia. Based on a thorough literature review with 5500 cited references and nearly 1000 figures and tables, the two books review in 26 chapters all major natural and man-made vegetation types with their climatic and edaphic influences, the structure and dynamics of their communities, the ecophysiology of important plant species, and key aspects of ecosystem functioning. Volume I deals with the forests and scrub vegetation and analyses the ecology of Central Europe’s tree flora, whilst Volume II is dedicated to the non-forest vegetation covering mires, grasslands, heaths, alpine habitats and urban vegetation. The consequences of over-use, pollution and recent climate change over the last century are explored and conservation issues addressed.
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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