메뉴 건너뛰기




Volumn , Issue , 2010, Pages 219-248

Statistical Resampling Techniques for Large Biological Data Analysis

Author keywords

Feature selection improving prediction performance and reducing computational intensity; Large biological data analysis, statistical resampling techniques; Model selection and performance assessment evaluating prediction rule error rate

Indexed keywords


EID: 84860404184     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470567647.ch10     Document Type: Chapter
Times cited : (5)

References (49)
  • 1
    • 13844316757 scopus 로고    scopus 로고
    • Signal in noise: Evaluating reported reproducibility of serum proteomic tests for ovarian cancer
    • Baggerly, K. A., et al. (2005). Signal in noise: Evaluating reported reproducibility of serum proteomic tests for ovarian cancer. J. Nat. Cancer Inst., 97(4): 307-309.
    • (2005) J. Nat. Cancer Inst. , vol.97 , Issue.4 , pp. 307-309
    • Baggerly, K.A.1
  • 2
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • Breiman, L. (2001). Random forests. Machine Learning, 45: 5-32.
    • (2001) Machine Learning , vol.45 , pp. 5-32
    • Breiman, L.1
  • 3
    • 0003802343 scopus 로고
    • Classification and Regression Trees
    • Monterey, CA: Wadsworth and Brooks/Cole.
    • Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984). Classification and Regression Trees. Monterey, CA: Wadsworth and Brooks/Cole.
    • (1984)
    • Breiman, L.1    Friedman, J.H.2    Olshen, R.A.3    Stone, C.J.4
  • 4
    • 0000343716 scopus 로고
    • Submodel selection and evaluation in regression. The X-random case
    • Breiman, L., and Spector, P. (1992). Submodel selection and evaluation in regression. The X-random case. Int. Stat. Rev., 60: 291-319.
    • (1992) Int. Stat. Rev. , vol.60 , pp. 291-319
    • Breiman, L.1    Spector, P.2
  • 5
    • 0000354976 scopus 로고
    • A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods
    • Burman, P. (1989). A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika, 76: 503-514.
    • (1989) Biometrika , vol.76 , pp. 503-514
    • Burman, P.1
  • 6
    • 84886499957 scopus 로고
    • Explaining the Gibbs sampler
    • Casella, G., and George, E. I. (1991). Explaining the Gibbs sampler. Am. Stat., 26: 16-174.
    • (1991) Am. Stat. , vol.26 , pp. 16-174
    • Casella, G.1    George, E.I.2
  • 7
    • 32344446687 scopus 로고
    • Understanding the Metropolis-Hastings Algorithm
    • Chib, S., and Greenberg, E. (1995). Understanding the Metropolis-Hastings Algorithm. Am. Stat., 49(4): 327-335.
    • (1995) Am. Stat. , vol.49 , Issue.4 , pp. 327-335
    • Chib, S.1    Greenberg, E.2
  • 8
    • 84886496188 scopus 로고    scopus 로고
    • Bootstrap Methods and Their Application. Cambridge Series in Statistical and Probabilistic Mathematics
    • Cambridge: Cambridge University Press.
    • Davison, A. C., and Hinkley, D. V. (1997). Bootstrap Methods and Their Application. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge: Cambridge University Press.
    • (1997) Davison, A. C., and Hinkley, D. V.
  • 9
    • 26644446561 scopus 로고    scopus 로고
    • Asymptotics of cross-validated risk estimation in model selection and performance assessment
    • Dudoit, S., and van der Laan, M. J. (2005). Asymptotics of cross-validated risk estimation in model selection and performance assessment. Stat. Methodol., 2(2): 131-154.
    • (2005) Stat. Methodol. , vol.2 , Issue.2 , pp. 131-154
    • Dudoit, S.1    van der Laan, M.J.2
  • 10
    • 84950461478 scopus 로고
    • Estimating the error rate of a prediction rule: Improvement on cross-validation
    • Efron, B. (1983). Estimating the error rate of a prediction rule: Improvement on cross-validation. J. Am. Stat. Assoc., 78: 316-331.
    • (1983) J. Am. Stat. Assoc. , vol.78 , pp. 316-331
    • Efron, B.1
  • 11
    • 4944239996 scopus 로고    scopus 로고
    • The estimation of prediction error: Covariance penalties and cross-validation
    • Efron, B. (2004). The estimation of prediction error: Covariance penalties and cross-validation. J. Am. Stat. Assoc., 99: 619-642.
    • (2004) J. Am. Stat. Assoc. , vol.99 , pp. 619-642
    • Efron, B.1
  • 12
    • 84886472543 scopus 로고
    • An Introduction to the Bootstrap. Monographs on Statistics and Applied Probability
    • New York: Chapman & Hall.
    • Efron, B., and Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Monographs on Statistics and Applied Probability, Vol. 57. New York: Chapman & Hall.
    • (1993) Efron, B., and Tibshirani, R. J. , vol.57
  • 13
    • 0031536511 scopus 로고    scopus 로고
    • Improvements on cross-validation: The .632 bootstrap method
    • Efron, B., and Tibshirani, R. J. (1997). Improvements on cross-validation: The .632 bootstrap method. J. Am. Stat. Assoc., 92: 548-560.
    • (1997) J. Am. Stat. Assoc. , vol.92 , pp. 548-560
    • Efron, B.1    Tibshirani, R.J.2
  • 14
    • 84950645271 scopus 로고
    • The predictive sample reuse method with applications
    • Geisser, S. (1975). The predictive sample reuse method with applications. J. Am. Stat. Assoc., 70: 320-328.
    • (1975) J. Am. Stat. Assoc. , vol.70 , pp. 320-328
    • Geisser, S.1
  • 15
    • 84950453304 scopus 로고
    • Sampling based approaches to calculating marginal densities
    • Gelfand, A., and Smith, A. (1990). Sampling based approaches to calculating marginal densities. J. Am. Stat. Assoc., 85: 398-409.
    • (1990) J. Am. Stat. Assoc. , vol.85 , pp. 398-409
    • Gelf, A.1    Smith, A.2
  • 16
    • 0004012196 scopus 로고    scopus 로고
    • Bayesian Data Analysis
    • Boca Raton, FL: Chapman & Hall.
    • Gelman, A., et al. (2003). Bayesian Data Analysis. Boca Raton, FL: Chapman & Hall.
    • (2003)
    • Gelman, A.1
  • 17
    • 0021518209 scopus 로고
    • Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
    • Geman, S., and Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Machine Intell., 6: 721-741.
    • (1984) IEEE Trans. Pattern Anal. Machine Intell. , vol.6 , pp. 721-741
    • Geman, S.1    Geman, D.2
  • 18
    • 84886489957 scopus 로고    scopus 로고
    • The Elements of Statistical Learning: Data Mining, Inference, and Prediction
    • Springer Series in Statistics, 1st ed. New York: Springer.
    • Hastie, T., Tibshirani, R., and Friedman, J. (2003). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics, 1st ed. New York: Springer.
    • (2003) Hastie, T., Tibshirani, R., and Friedman, J.
  • 19
    • 77956890234 scopus 로고
    • Monte Carlo sampling methods using Markov chains and their applications
    • Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1): 97-109.
    • (1970) Biometrika , vol.57 , Issue.1 , pp. 97-109
    • Hastings, W.K.1
  • 20
    • 0346840425 scopus 로고    scopus 로고
    • Detecting recombination with MCMC
    • Husmeier, D., and McGuire, G. (2002). Detecting recombination with MCMC. Bioinformatics, 18: S345-S353.
    • (2002) Bioinformatics , vol.18
    • Husmeier, D.1    McGuire, G.2
  • 21
    • 37349009294 scopus 로고    scopus 로고
    • A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in microarray classification
    • Jiang, W., and Simon, R. (2007). A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in microarray classification. Stat. Med., 26: 5320-5334.
    • (2007) Stat. Med. , vol.26 , pp. 5320-5334
    • Jiang, W.1    Simon, R.2
  • 23
    • 84950424966 scopus 로고
    • Bayesian models for multiple local sequence alignment and Gibbs sampling strategies
    • Liu, L. S., Neuwald, A. F., and Lawrence, C. E. (1995). Bayesian models for multiple local sequence alignment and Gibbs sampling strategies. JASA, 90: 1156-1170.
    • (1995) JASA , vol.90 , pp. 1156-1170
    • Liu, L.S.1    Neuwald, A.F.2    Lawrence, C.E.3
  • 24
    • 33745147888 scopus 로고    scopus 로고
    • Assessing performance of prediction rules in machine learning
    • 5 Jun 2006. 7 Oct 2009
    • Martin, R., and Yu, K. (2006). Assessing performance of prediction rules in machine learning. 5 Jun 2006. 7 Oct 2009 ,http://www.futuremedicine.com/doi/abs/10.2217/14622416.7.4.543..
    • (2006) Martin, R., and Yu, K.
  • 25
    • 84886535370 scopus 로고
    • Discriminant Analysis and Statistical Pattern Recognition
    • New York: Wiley.
    • McLachlan, G. J. (1992). Discriminant Analysis and Statistical Pattern Recognition. New York: Wiley.
    • (1992) McLachlan, G. J.
  • 26
    • 0036203115 scopus 로고    scopus 로고
    • A mixture model-based approach to the clustering of microarray expression data
    • McLachlan, G. J., Bean, R. W., and Peel, D. (2002). A mixture model-based approach to the clustering of microarray expression data. Bioinformatics, 18(3): 413-422.
    • (2002) Bioinformatics , vol.18 , Issue.3 , pp. 413-422
    • McLachlan, G.J.1    Bean, R.W.2    Peel, D.3
  • 28
    • 23744457899 scopus 로고    scopus 로고
    • Tree-based multivariate regression and density estimation with right-censored Data
    • Molinaro, A. M., Dudoit, S., and van der Laan, M. J. (2004). Tree-based multivariate regression and density estimation with right-censored Data. J. Multivariate Anal., 90: 154-177.
    • (2004) J. Multivariate Anal. , vol.90 , pp. 154-177
    • Molinaro, A.M.1    Dudoit, S.2    van der Laan, M.J.3
  • 29
    • 33846849378 scopus 로고    scopus 로고
    • Prediction error estimation: A comparison of resampling methods
    • Molinaro, A. M., Simon, R., and Pfeiffer, R. M. (2005). Prediction error estimation: A comparison of resampling methods. Bioinformatics, 21: 309-313.
    • (2005) Bioinformatics , vol.21 , pp. 309-313
    • Molinaro, A.M.1    Simon, R.2    Pfeiffer, R.M.3
  • 30
    • 0037116832 scopus 로고    scopus 로고
    • Use of proteomic patters in serum to identify ovarian cancer
    • Petricoin, E. F., et al. (2002). Use of proteomic patters in serum to identify ovarian cancer. Lancet, 359: 572-577.
    • (2002) Lancet , vol.359 , pp. 572-577
    • Petricoin, E.F.1
  • 31
    • 84886450585 scopus 로고    scopus 로고
    • New cancer test stirs hope and concern
    • New York Times, February 3.
    • Pollack, A. (2004). New cancer test stirs hope and concern. New York Times, February 3.
    • (2004) Pollack, A.
  • 32
    • 26444492840 scopus 로고    scopus 로고
    • Meeting the challenges of functional genomics: From the laboratory to the clinic
    • Quackenbush, J. (2004). Meeting the challenges of functional genomics: From the laboratory to the clinic. Preclinica, 2: 313-316.
    • (2004) Preclinica , vol.2 , pp. 313-316
    • Quackenbush, J.1
  • 33
    • 1942438016 scopus 로고    scopus 로고
    • Rules of evidence for cancer molecular marker discovery and validation
    • Ransohoff, D. F. (2004). Rules of evidence for cancer molecular marker discovery and validation. Nat. Rev. Cancer, 4: 309-313.
    • (2004) Nat. Rev. Cancer , vol.4 , pp. 309-313
    • Ransohoff, D.F.1
  • 34
    • 13844322072 scopus 로고    scopus 로고
    • Lessons from controversy: Ovarian cancer screening and serum proteomics
    • Ransohoff, D. F. (2005). Lessons from controversy: Ovarian cancer screening and serum proteomics. JNCI, 97: 315-319.
    • (2005) JNCI , vol.97 , pp. 315-319
    • Ransohoff, D.F.1
  • 35
    • 84953405534 scopus 로고    scopus 로고
    • Pattern Recognition and Neural Networks
    • Cambridge: Cambridge University Press.
    • Ripley, B. D. (1996). Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press.
    • (1996) Ripley, B. D.
  • 36
    • 0001425735 scopus 로고
    • Recovery of information and adjustment for dependent censoring using surrogate markers
    • Robins, J. M., and Rotnitzky, A. (1992). Recovery of information and adjustment for dependent censoring using surrogate markers. Aids Epidemiology, Methodological Issues: 297-331.
    • (1992) Aids Epidemiology, Methodological Issues , pp. 297-331
    • Robins, J.M.1    Rotnitzky, A.2
  • 37
    • 0037142053 scopus 로고    scopus 로고
    • The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma
    • Rosenwald, A., et al. (2002). The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N. Engl. J. Med., 346: 1937-1946.
    • (2002) N. Engl. J. Med. , vol.346 , pp. 1937-1946
    • Rosenwald, A.1
  • 39
    • 84886466432 scopus 로고    scopus 로고
    • Discovering Molecular Pathways from Protein Interaction and Gene Expression Data
    • Oxford: Oxford University Press.
    • Segal, E., Wang, H., and Koller, D. (2003). Discovering Molecular Pathways from Protein Interaction and Gene Expression Data. Oxford: Oxford University Press.
    • (2003) Segal, E., Wang, H., and Koller, D.
  • 40
    • 4444367133 scopus 로고    scopus 로고
    • Biclustering microarray data by Gibbs sampling
    • Sheng, Q., Moreau, Y., and De Moor, B. (2003). Biclustering microarray data by Gibbs sampling. Bioinformatics, 19(Suppl. 2): II196-II205.
    • (2003) Bioinformatics , vol.19 , Issue.2
    • Sheng, Q.1    Moreau, Y.2    De Moor, B.3
  • 41
    • 0037245343 scopus 로고    scopus 로고
    • Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification
    • Simon, R., Radmacher, M. D., Dobbin, K., and McShane, L. M. (2003). Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J. Nat. Cancer Inst., 95: 14-18.
    • (2003) J. Nat. Cancer Inst. , vol.95 , pp. 14-18
    • Simon, R.1    Radmacher, M.D.2    Dobbin, K.3    McShane, L.M.4
  • 42
    • 0000629975 scopus 로고
    • Cross-validatory choice and assessment of statistical predictions
    • Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc. Ser. B, 36: 111-147.
    • (1974) J. R. Stat. Soc. Ser. B , vol.36 , pp. 111-147
    • Stone, M.1
  • 43
    • 0017336301 scopus 로고
    • Asymptotics for and against cross-validation
    • Stone, M. (1977). Asymptotics for and against cross-validation. Biometrika, 64: 29-35.
    • (1977) Biometrika , vol.64 , pp. 29-35
    • Stone, M.1
  • 44
    • 0003516711 scopus 로고    scopus 로고
    • An introduction to recursive partitioning using the RPART routine
    • Technical Report 61. Section of Biostatistics, Mayo Clinic, Rochester, NY.
    • Therneau, T., and Atkinson, E. (1997). An introduction to recursive partitioning using the RPART routine. Technical Report 61. Section of Biostatistics, Mayo Clinic, Rochester, NY.
    • (1997)
    • Therneau, T.1    Atkinson, E.2
  • 45
    • 85194972808 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso
    • Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B, 58(1): 267-288.
    • (1996) J. R. Stat. Soc. B , vol.58 , Issue.1 , pp. 267-288
    • Tibshirani, R.1
  • 46
    • 0037137519 scopus 로고    scopus 로고
    • A gene expression signature as a predictor of survival in breast cancer
    • van de Vijver, M. J., et al. (2002). A gene expression signature as a predictor of survival in breast cancer. N. Engl. J. Med., 347(25): 1999-2009.
    • (2002) N. Engl. J. Med. , vol.347 , Issue.25 , pp. 1999-2009
    • van de Vijver, M.J.1
  • 47
    • 33644860703 scopus 로고    scopus 로고
    • Bias in error estimation when using cross-validation for model selection
    • Varma, S., and Simon, R. (2006). Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics, 7: 91.
    • (2006) BMC Bioinformatics , vol.7 , pp. 91
    • Varma, S.1    Simon, R.2
  • 48
    • 0003895851 scopus 로고
    • Modern Applied Statistics with S-PLUS
    • New York: Springer- Verlag.
    • Venables, W. N., and Ripley, B. D. (1994). Modern Applied Statistics with S-PLUS. New York: Springer- Verlag.
    • (1994)
    • Venables, W.N.1    Ripley, B.D.2
  • 49
    • 0043192901 scopus 로고    scopus 로고
    • A gene expressionbased method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma
    • Wright, G., Tan, B., Rosenwald, A., Hurt, E. H.,Wiestner, A., and Staudt, L. M. (2003). A gene expressionbased method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma. Proc. Natl. Acad. Sci. U.S.A., 100: 9991-9996.
    • (2003) Proc. Natl. Acad. Sci. U.S.A. , vol.100 , pp. 9991-9996
    • Wright, G.1    Tan, B.2    Rosenwald, A.3    Hurt, E.H.4    Wiestner, A.5    Staudt, L.M.6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.