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Volumn 2, Issue 4, 2005, Pages 249-266

Cross validation model selection criteria for linear regression based on the Kullback-Leibler discrepancy

Author keywords

AIC; AICc; Akaike information criterion; Kullback Leibler information

Indexed keywords


EID: 33644771361     PISSN: 15723127     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.stamet.2005.05.002     Document Type: Article
Times cited : (15)

References (16)
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    • Cavanaugh, J.E.1
  • 7
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    • On the minimum variance unbiasedness property of AICc and MCp
    • Department of Biostatistics, The University of Iowa
    • S.L. Davies, A.A. Neath, J.E. Cavanaugh, On the minimum variance unbiasedness property of AICc and MCp, Technical Report, Department of Biostatistics, The University of Iowa, 2005
    • (2005) Technical Report
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  • 8
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    • B. Efron Estimating the error rate of a prediction rule: Improvement on cross-validation Journal of the American Statistical Association 78 1983 316 331
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    • How biased is the apparent error rate of a prediction rule?
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    • Efron, B.1
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    • Hurvich, C.M.1    Tsai, C.L.2
  • 16
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    • Further analysis of the data by Akaike's information criterion and the finite corrections
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    • Sugiura, N.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.