Multivariate prediction: Selection of the most informative components to measure

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A number of interesting problems in the design of experiments such as sensor allocation, selection of sites for the observing stations, determining sampler positions in traffic monitoring, and which variables to survey/measure in sampling studies may be considered in the following setting: Given a covariance matrix of multi-dimension random vector and given a ratio of the number of possible observations to the observational error select those components which must be observed to guarantee minimization of an objective function describing the quality of prediction of all or prescribed components. The authors show that the problem can be considered in the framework ... continued below

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10 p.

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Batsell, S.; Fedorov, V. & Flanagan, D. June 1, 1998.

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Description

A number of interesting problems in the design of experiments such as sensor allocation, selection of sites for the observing stations, determining sampler positions in traffic monitoring, and which variables to survey/measure in sampling studies may be considered in the following setting: Given a covariance matrix of multi-dimension random vector and given a ratio of the number of possible observations to the observational error select those components which must be observed to guarantee minimization of an objective function describing the quality of prediction of all or prescribed components. The authors show that the problem can be considered in the framework of convex design theory and derive some simple but effective algorithm for selection of an optimal subset of components to be observed.

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10 p.

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OSTI as DE97007775

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  • MODA 5: a workshop on statistics and experimental design, Marseilles (France), 26-30 Jun 1998

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  • Other: DE97007775
  • Report No.: CONF-980607--1
  • Grant Number: AC05-96OR22464
  • Office of Scientific & Technical Information Report Number: 650267
  • Archival Resource Key: ark:/67531/metadc711497

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  • June 1, 1998

Added to The UNT Digital Library

  • Sept. 12, 2015, 6:31 a.m.

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  • Jan. 19, 2016, 8:23 p.m.

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Batsell, S.; Fedorov, V. & Flanagan, D. Multivariate prediction: Selection of the most informative components to measure, article, June 1, 1998; Tennessee. (digital.library.unt.edu/ark:/67531/metadc711497/: accessed October 19, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.