Aspects of model selection in multivariate analyses

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Analysis of data sets that involve large numbers of variables usually entails some type of model fitting and data reduction. In regression problems, a fitted model that is obtained by a selection process can be difficult to evaluate because of optimism induced by the choice mechanism. Problems in areas such as discriminant analysis, calibration, and the like often lead to similar difficulties. The preceeding sections reviewed some of the general ideas behind assessment of regression-type predictors and illustrated how they can be easily incorporated into a standard data analysis.

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Pages: 15

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Picard, R. January 1, 1982.

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Analysis of data sets that involve large numbers of variables usually entails some type of model fitting and data reduction. In regression problems, a fitted model that is obtained by a selection process can be difficult to evaluate because of optimism induced by the choice mechanism. Problems in areas such as discriminant analysis, calibration, and the like often lead to similar difficulties. The preceeding sections reviewed some of the general ideas behind assessment of regression-type predictors and illustrated how they can be easily incorporated into a standard data analysis.

Physical Description

Pages: 15

Notes

NTIS, PC A02/MF A01.

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  • DOE statistical symposium, Idaho Falls, ID, USA, 1 Oct 1982

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  • Other: DE82019576
  • Report No.: LA-UR-82-1893
  • Report No.: CONF-821021-1
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 5018227
  • Archival Resource Key: ark:/67531/metadc1056807

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  • January 1, 1982

Added to The UNT Digital Library

  • Jan. 22, 2018, 7:23 a.m.

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  • Feb. 1, 2018, 6:59 p.m.

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Picard, R. Aspects of model selection in multivariate analyses, article, January 1, 1982; New Mexico. (digital.library.unt.edu/ark:/67531/metadc1056807/: accessed October 22, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.