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Volumn 1, Issue 1, 1997, Pages 11-28

Statistical themes and lessons for data mining

Author keywords

Bias; Modeling; Statistics; Uncertainty; Variance

Indexed keywords


EID: 21744454917     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1009773905005     Document Type: Article
Times cited : (133)

References (27)
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  • 3
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    • to appear
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    • Model uncertainty, data mining, and statistical inference
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  • 7
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    • Informative drop-out in longitudinal data analysis
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    • Diggle, P.1    Kenward, M.G.2
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    • Freedman, D.A.1
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    • Skills for industrial statisticians to survive and prosper in the emerging quality environment
    • Hoerl, R.W., Hooper, J.H., Jacobs, P.J., and Lucas, J.M. 1993. Skills for industrial statisticians to survive and prosper in the emerging quality environment. The American Statistician, 47:280-292.
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    • Hoerl, R.W.1    Hooper, J.H.2    Jacobs, P.J.3    Lucas, J.M.4
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    • Some general points in probability theory
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    • Jeffreys, H. 1980. Some general points in probability theory. In Bayesian Analysis in Econometrics and Statistics, A. Zellner (Ed.). Amsterdam: North-Holland, 451-454.
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    • Model selection and accounting for model uncertainty in graphical models using Occam's Window
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  • 27
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    • Bayesian graphical models for discrete data
    • Madigan, D. and York, J. 1995. Bayesian graphical models for discrete data. International Statistical Review, 63:215-232.
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    • Madigan, D.1    York, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.