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Volumn 116, Issue , 2015, Pages 248-254

Cross-validation and hypothesis testing in neuroimaging: An irenic comment on the exchange between Friston and Lindquist et al.

Author keywords

[No Author keywords available]

Indexed keywords

BOLD SIGNAL; DATA ANALYSIS; HUMAN; IMAGE ANALYSIS; IMAGING SYSTEM; MATHEMATICAL PARAMETERS; MAXIMUM LIKELIHOOD METHOD; MEASUREMENT ACCURACY; MEASUREMENT ERROR; NEUROIMAGING; NOTE; NULL HYPOTHESIS; PRIORITY JOURNAL; RADIOLOGICAL PROCEDURES; STATISTICAL MODEL; VALIDATION STUDY; METHODOLOGY; PEER REVIEW; PROCEDURES; STATISTICS;

EID: 84941744520     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2015.04.032     Document Type: Note
Times cited : (14)

References (48)
  • 3
    • 77956649096 scopus 로고    scopus 로고
    • A survey of cross-validation procedures for model selection
    • Arlot S., Celisse A. A survey of cross-validation procedures for model selection. Stat. Surv. 2010, 4:40-79.
    • (2010) Stat. Surv. , vol.4 , pp. 40-79
    • Arlot, S.1    Celisse, A.2
  • 4
    • 79955757684 scopus 로고    scopus 로고
    • Added predictive value of high-throughput molecular data to clinical data and its validation
    • Boulesteix A.L., Sauerbrei W. Added predictive value of high-throughput molecular data to clinical data and its validation. Brief. Bioinform. 2011, 12:215-229.
    • (2011) Brief. Bioinform. , vol.12 , pp. 215-229
    • Boulesteix, A.L.1    Sauerbrei, W.2
  • 5
    • 0000245743 scopus 로고    scopus 로고
    • Statistical modeling: the two cultures (with discussion)
    • Breiman L. Statistical modeling: the two cultures (with discussion). Stat. Sci. 2001, 16:199-231.
    • (2001) Stat. Sci. , vol.16 , pp. 199-231
    • Breiman, L.1
  • 9
    • 1042279287 scopus 로고    scopus 로고
    • Likelihood ratio tests in linear mixed models with one variance component
    • Crainiceanu C.M., Ruppert D. Likelihood ratio tests in linear mixed models with one variance component. J. R. Stat. Soc. Ser. B 2004, 66:165-185.
    • (2004) J. R. Stat. Soc. Ser. B , vol.66 , pp. 165-185
    • Crainiceanu, C.M.1    Ruppert, D.2
  • 11
    • 77950537175 scopus 로고    scopus 로고
    • Regularization paths for generalized linear models via coordinate descent
    • Friedman J., Hastie T., Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 2010, 33:1.
    • (2010) J. Stat. Softw. , vol.33 , pp. 1
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 12
    • 84861331040 scopus 로고    scopus 로고
    • Ten ironic rules for non-statistical reviewers
    • Friston K. Ten ironic rules for non-statistical reviewers. NeuroImage 2012, 61:1300-1310.
    • (2012) NeuroImage , vol.61 , pp. 1300-1310
    • Friston, K.1
  • 13
    • 84880847793 scopus 로고    scopus 로고
    • Sample size and the fallacies of classical inference
    • Friston K. Sample size and the fallacies of classical inference. NeuroImage 2013, 81:503-504.
    • (2013) NeuroImage , vol.81 , pp. 503-504
    • Friston, K.1
  • 17
    • 84872981771 scopus 로고    scopus 로고
    • Reanalysis of "Bedside detection of awareness in the vegetative state: a cohort study"
    • Goldfine A.M., Bardin J.C., Noirhomme Q., Fins J.J., Schiff N.D., Victor J.D. Reanalysis of "Bedside detection of awareness in the vegetative state: a cohort study". Lancet 2013, 381:289-291.
    • (2013) Lancet , vol.381 , pp. 289-291
    • Goldfine, A.M.1    Bardin, J.C.2    Noirhomme, Q.3    Fins, J.J.4    Schiff, N.D.5    Victor, J.D.6
  • 18
  • 21
    • 0000069803 scopus 로고
    • The large-sample power of tests based on permutations of observations
    • Hoeffding W. The large-sample power of tests based on permutations of observations. Ann. Math. Stat. 1952, 23:169-192.
    • (1952) Ann. Math. Stat. , vol.23 , pp. 169-192
    • Hoeffding, W.1
  • 22
    • 0038047907 scopus 로고    scopus 로고
    • Relation between permutation-test p values and classifier error estimates
    • Hsing T., Attoor S., Dougherty E. Relation between permutation-test p values and classifier error estimates. Mach. Learn. 2003, 52:11-30.
    • (2003) Mach. Learn. , vol.52 , pp. 11-30
    • Hsing, T.1    Attoor, S.2    Dougherty, E.3
  • 23
    • 84880811845 scopus 로고    scopus 로고
    • Why small low-powered studies are worse than large high-powered studies and how to protect against "trivial" findings in research: comment on Friston (2012)
    • Ingre M. Why small low-powered studies are worse than large high-powered studies and how to protect against "trivial" findings in research: comment on Friston (2012). NeuroImage 2013, 81:496-498.
    • (2013) NeuroImage , vol.81 , pp. 496-498
    • Ingre, M.1
  • 26
    • 84871997442 scopus 로고    scopus 로고
    • Functional causal mediation analysis with an application to brain connectivity
    • Lindquist M.A. Functional causal mediation analysis with an application to brain connectivity. J. Am. Stat. Assoc. 2012, 107:1297-1309.
    • (2012) J. Am. Stat. Assoc. , vol.107 , pp. 1297-1309
    • Lindquist, M.A.1
  • 27
    • 84880812523 scopus 로고    scopus 로고
    • Ironing out the statistical wrinkles in "ten ironic rules"
    • Lindquist M.A., Caffo B., Crainiceanu C. Ironing out the statistical wrinkles in "ten ironic rules". NeuroImage 2013, 81:499-502.
    • (2013) NeuroImage , vol.81 , pp. 499-502
    • Lindquist, M.A.1    Caffo, B.2    Crainiceanu, C.3
  • 29
    • 0000776754 scopus 로고
    • On the problem of the most efficient tests of statistical hypotheses
    • Neyman J., Pearson E.S. On the problem of the most efficient tests of statistical hypotheses. Philos. Trans. R. Soc. Lond. Ser. A 1933, 231:289-337.
    • (1933) Philos. Trans. R. Soc. Lond. Ser. A , vol.231 , pp. 289-337
    • Neyman, J.1    Pearson, E.S.2
  • 30
    • 0036136472 scopus 로고    scopus 로고
    • Nonparametric permutation tests for functional neuroimaging: a primer with examples
    • Nichols T.E., Holmes A.P. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum. Brain Mapp. 2001, 15:1-25.
    • (2001) Hum. Brain Mapp. , vol.15 , pp. 1-25
    • Nichols, T.E.1    Holmes, A.P.2
  • 32
    • 77954676863 scopus 로고    scopus 로고
    • Permutation tests for studying classifier performance
    • Ojala M., Garriga G. Permutation tests for studying classifier performance. J. Mach. Learn. Res. 2010, 11:1833-1863.
    • (2010) J. Mach. Learn. Res. , vol.11 , pp. 1833-1863
    • Ojala, M.1    Garriga, G.2
  • 34
    • 80054120233 scopus 로고    scopus 로고
    • Comparing dynamic causal models using AIC, BIC and free energy
    • Penny W. Comparing dynamic causal models using AIC, BIC and free energy. NeuroImage 2012, 59:319-330.
    • (2012) NeuroImage , vol.59 , pp. 319-330
    • Penny, W.1
  • 35
    • 84876321464 scopus 로고    scopus 로고
    • Testing for improvement in prediction model performance
    • Pepe M.S., Kerr K.F., Longton G., Wang Z. Testing for improvement in prediction model performance. Stat. Med. 2013, 32:1467-1482.
    • (2013) Stat. Med. , vol.32 , pp. 1467-1482
    • Pepe, M.S.1    Kerr, K.F.2    Longton, G.3    Wang, Z.4
  • 36
    • 78149276744 scopus 로고    scopus 로고
    • Permutation p-values should never be zero: calculating exact p-values when permutations are randomly drawn
    • Phipson B., Smyth G.K. Permutation p-values should never be zero: calculating exact p-values when permutations are randomly drawn. Stat. Appl. Genet. Mol. Biol. 2010, 9.
    • (2010) Stat. Appl. Genet. Mol. Biol. , vol.9
    • Phipson, B.1    Smyth, G.K.2
  • 37
    • 84863304598 scopus 로고    scopus 로고
    • R Foundation for Statistical Computing, Vienna, Austria
    • R Core Team R: A Language and Environment for Statistical Computing 2014, R Foundation for Statistical Computing, Vienna, Austria, (URL: http://www.R-project.org/).
    • (2014) R: A Language and Environment for Statistical Computing
  • 38
    • 62849093921 scopus 로고    scopus 로고
    • Smoothing parameter selection for a class of semiparametric linear models
    • Reiss P.T., Ogden R.T. Smoothing parameter selection for a class of semiparametric linear models. J. R. Stat. Soc. Ser. B 2009, 71:505-523.
    • (2009) J. R. Stat. Soc. Ser. B , vol.71 , pp. 505-523
    • Reiss, P.T.1    Ogden, R.T.2
  • 39
    • 84938495493 scopus 로고    scopus 로고
    • Wavelet-domain regression and predictive inference in psychiatric neuroimaging
    • (in press)
    • Reiss P.T., Huo L., Zhao Y., Kelly C., Ogden R.T. Wavelet-domain regression and predictive inference in psychiatric neuroimaging. Ann. Appl. Stat. 2015, (in press).
    • (2015) Ann. Appl. Stat.
    • Reiss, P.T.1    Huo, L.2    Zhao, Y.3    Kelly, C.4    Ogden, R.T.5
  • 40
    • 0040753815 scopus 로고
    • The large-sample power of permutation tests for randomization models
    • Robinson J. The large-sample power of permutation tests for randomization models. Ann. Stat. 1973, 1:291-296.
    • (1973) Ann. Stat. , vol.1 , pp. 291-296
    • Robinson, J.1
  • 42
    • 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., McShane L.M. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J. Natl. Cancer Inst. 2003, 95:14-18.
    • (2003) J. Natl. Cancer Inst. , vol.95 , pp. 14-18
    • Simon, R.1    Radmacher, M.D.2    Dobbin, K.3    McShane, L.M.4
  • 43
    • 0000629975 scopus 로고
    • Cross-validatory choice and assessment of statistical predictions (with discussion)
    • Stone M. Cross-validatory choice and assessment of statistical predictions (with discussion). J. R. Stat. Soc. Ser. B 1974, 36:111-147.
    • (1974) J. R. Stat. Soc. Ser. B , vol.36 , pp. 111-147
    • Stone, M.1
  • 44
    • 85194972808 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso
    • Tibshirani R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 1996, 58:267-288.
    • (1996) J. R. Stat. Soc. Ser. B , vol.58 , pp. 267-288
    • Tibshirani, R.1
  • 45
    • 70149099645 scopus 로고    scopus 로고
    • Testing the prediction error difference between 2 predictors
    • van de Wiel M.A., Berkhof J., van Wieringen W.N. Testing the prediction error difference between 2 predictors. Biostatistics 2009, 10:550-560.
    • (2009) Biostatistics , vol.10 , pp. 550-560
    • van de Wiel, M.A.1    Berkhof, J.2    van Wieringen, W.N.3
  • 46
    • 0001972601 scopus 로고
    • The large-sample distribution of the likelihood ratio for testing composite hypotheses
    • Wilks S.S. The large-sample distribution of the likelihood ratio for testing composite hypotheses. Ann. Math. Stat. 1938, 9:60-62.
    • (1938) Ann. Math. Stat. , vol.9 , pp. 60-62
    • Wilks, S.S.1
  • 48
    • 78650862532 scopus 로고    scopus 로고
    • Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models
    • Wood S.N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Ser. B 2011, 73:3-36.
    • (2011) J. R. Stat. Soc. Ser. B , vol.73 , pp. 3-36
    • Wood, S.N.1


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