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Volumn 21, Issue 4, 2016, Pages 583-602

Finding structure in data using multivariate tree boosting

Author keywords

Boosting; Decision trees; Model selection; Multivariate; Nonparametric regression

Indexed keywords

DECISION TREE; HUMAN; MODEL; MULTIPLE REGRESSION; OUTCOME VARIABLE; PREDICTION; PSYCHOLOGICAL WELL-BEING; SAMPLE SIZE; ALGORITHM; INFORMATION PROCESSING; MULTIVARIATE ANALYSIS;

EID: 85004115628     PISSN: 1082989X     EISSN: None     Source Type: Journal    
DOI: 10.1037/met0000087     Document Type: Article
Times cited : (60)

References (96)
  • 1
    • 0024308522 scopus 로고
    • The impact of a military air disaster on the health of assistance workers. A prospective study
    • Bartone, P. T., Ursano, R. J., Wright, K. M., & Ingraham, L. H. (1989). The impact of a military air disaster on the health of assistance workers. A prospective study. Journal of Nervous and Mental Disease, 177, 317-328. http://dx.doi.org/10.1097/00005053-198906000-00001
    • (1989) Journal of Nervous and Mental Disease , vol.177 , pp. 317-328
    • Bartone, P.T.1    Ursano, R.J.2    Wright, K.M.3    Ingraham, L.H.4
  • 4
    • 84900801998 scopus 로고    scopus 로고
    • Trait stress resistance and dynamic stress dissipation on health and well-being: The reservoir model
    • Bergeman, C. S., & Deboeck, P. R. (2014). Trait stress resistance and dynamic stress dissipation on health and well-being: The reservoir model. Research in Human Development, 11, 108-125. http://dx.doi.org/10.1080/15427609.2014.906736
    • (2014) Research in Human Development , vol.11 , pp. 108-125
    • Bergeman, C.S.1    Deboeck, P.R.2
  • 5
    • 0030076540 scopus 로고    scopus 로고
    • IQ and ego-resiliency: Conceptual and empirical connections and separateness
    • Block, J., & Kremen, A. M. (1996). IQ and ego-resiliency: Conceptual and empirical connections and separateness. Journal of Personality and Social Psychology, 70, 349-361. http://dx.doi.org/10.1037/0022-3514.70.2.349
    • (1996) Journal of Personality and Social Psychology , vol.70 , pp. 349-361
    • Block, J.1    Kremen, A.M.2
  • 6
    • 0031191630 scopus 로고    scopus 로고
    • The use of the area under the ROC curve in the evaluation of machine learning algorithms
    • Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30, 1145-1159. http://dx.doi.org/10.1016/S0031-3203(96)00142-2
    • (1997) Pattern Recognition , vol.30 , pp. 1145-1159
    • Bradley, A.P.1
  • 9
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123-140. http://dx.doi.org/10.1007/BF00058655
    • (1996) Machine Learning , vol.24 , pp. 123-140
    • Breiman, L.1
  • 10
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. http://dx.doi.org/10.1023/A:1010933404324
    • (2001) Machine Learning , vol.45 , pp. 5-32
    • Breiman, L.1
  • 12
    • 0001858087 scopus 로고
    • Multivariate decision trees
    • Brodley, C. E., & Utgoff, P. E. (1995). Multivariate decision trees. Machine Learning, 19, 45-77. http://dx.doi.org/10.1007/BF00994660
    • (1995) Machine Learning , vol.19 , pp. 45-77
    • Brodley, C.E.1    Utgoff, P.E.2
  • 13
    • 0030166516 scopus 로고    scopus 로고
    • Classification trees with optimal multivariate decision nodes
    • Brown, D. E., Pittard, C. L., & Park, H. (1996). Classification trees with optimal multivariate decision nodes. Pattern Recognition Letters, 17, 699-703. http://dx.doi.org/10.1016/0167-8655(96)00033-5
    • (1996) Pattern Recognition Letters , vol.17 , pp. 699-703
    • Brown, D.E.1    Pittard, C.L.2    Park, H.3
  • 14
    • 41549141939 scopus 로고    scopus 로고
    • Boosting algorithms: Regularization, prediction and model fitting
    • Bůhlmann, P., & Hothorn, T. (2007). Boosting algorithms: Regularization, prediction and model fitting. Statistical Science, 22, 477-505. http://dx.doi.org/10.1214/07-STS242
    • (2007) Statistical Science , vol.22 , pp. 477-505
    • Bůhlmann, P.1    Hothorn, T.2
  • 15
    • 0043245810 scopus 로고    scopus 로고
    • Boosting with the L2 loss: Regression and classification
    • Bůhlmann, P., & Yu, B. (2003). Boosting with the L2 loss: Regression and classification. Journal ofthe American Statistical Association, 98, 324-339. http://dx.doi.org/10.1198/016214503000125
    • (2003) Journal ofthe American Statistical Association , vol.98 , pp. 324-339
    • Bůhlmann, P.1    Yu, B.2
  • 21
    • 0011153447 scopus 로고    scopus 로고
    • Multivariate regression trees: A new technique for modeling species-environment relationships
    • De'Ath, G. (2002). Multivariate regression trees: A new technique for modeling species-environment relationships. Ecology, 83, 1105-1117.
    • (2002) Ecology , vol.83 , pp. 1105-1117
    • De'Ath, G.1
  • 23
    • 0000935603 scopus 로고
    • Center for epidemiologic studies depression scale
    • Devins, G. M., & Orme, C. M. (1985). Center for epidemiologic studies depression scale. Test Critiques, 20, 144-160.
    • (1985) Test Critiques , vol.20 , pp. 144-160
    • Devins, G.M.1    Orme, C.M.2
  • 25
    • 44849118698 scopus 로고    scopus 로고
    • A working guide to boosted regression trees
    • Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal ofAnimal Ecology, 77, 802-813. http://dx.doi.org/10.1111/j.1365-2656.2008.01390.x
    • (2008) Journal ofAnimal Ecology , vol.77 , pp. 802-813
    • Elith, J.1    Leathwick, J.R.2    Hastie, T.3
  • 27
    • 58049204427 scopus 로고    scopus 로고
    • A multivariate test of association
    • Ferreira, M. A. R., & Purcell, S. M. (2009). A multivariate test of association. Bioinformatics, 25, 132-133. http://dx.doi.org/10.1093/bioinformatics/btn563
    • (2009) Bioinformatics , vol.25 , pp. 132-133
    • Ferreira, M.A.R.1    Purcell, S.M.2
  • 28
    • 77953774705 scopus 로고    scopus 로고
    • Multivariate decision trees using different splitting attribute subsets for large datasets
    • A. Farzindar & V. Kešelj (Eds.), Berlin, Germany: Springer
    • Franco-Arcega, A., Carrasco-Ochoa, J. A., Sánchez-Diaaz, G., & Martianez-Trinidad, J. F. (2010). Multivariate decision trees using different splitting attribute subsets for large datasets. In A. Farzindar & V. Kešelj (Eds.), Advances in artificial intelligence (Vol. 6085, pp. 370-373). Berlin, Germany: Springer. http://dx.doi.org/10.1007/978-3-642-13059-5_49
    • (2010) Advances in artificial intelligence , vol.6085 , pp. 370-373
    • Franco-Arcega, A.1    Carrasco-Ochoa, J.A.2    Sánchez-Diaaz, G.3    Martianez-Trinidad, J.F.4
  • 30
    • 0031211090 scopus 로고    scopus 로고
    • A decision-theoretic generalization of on-line learning and an application to boosting
    • Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal ofComputer and System Sciences, 55, 119-139. http://dx.doi.org/10.1006/jcss.1997.1504
    • (1997) Journal ofComputer and System Sciences , vol.55 , pp. 119-139
    • Freund, Y.1    Schapire, R.E.2
  • 31
    • 0035470889 scopus 로고    scopus 로고
    • Greedy function approximation: A gradient boosting machine
    • Friedman, J. (2001). Greedy function approximation: A gradient boosting machine. Annals ofStatistics, 29, 1189-1232. http://dx.doi.org/10.1214/aos/1013203451
    • (2001) Annals ofStatistics , vol.29 , pp. 1189-1232
    • Friedman, J.1
  • 32
    • 0037186544 scopus 로고    scopus 로고
    • Stochastic gradient boosting
    • Friedman, J. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38, 367-378. http://dx.doi.org/10.1016/S0167-9473(01)00065-2
    • (2002) Computational Statistics & Data Analysis , vol.38 , pp. 367-378
    • Friedman, J.1
  • 33
    • 0034164230 scopus 로고    scopus 로고
    • Additive logistic regression: A statistical view of boosting (With discussion and a rejoinder by the authors)
    • Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting (With discussion and a rejoinder by the authors). Annals of Statistics, 28, 337-407. http://dx.doi.org/10.1214/aos/1016218223
    • (2000) Annals of Statistics , vol.28 , pp. 337-407
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 34
    • 77950537175 scopus 로고    scopus 로고
    • Regularization paths for generalized linear models via coordinate descent
    • Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal ofStatistical Software, 33, 1-22. http://dx.doi.org/10.18637/jss.v033.i01
    • (2010) Journal ofStatistical Software , vol.33 , pp. 1-22
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 35
    • 0038702163 scopus 로고    scopus 로고
    • Multiple additive regression trees with application in epidemiology
    • Friedman, J. H., & Meulman, J. J. (2003). Multiple additive regression trees with application in epidemiology. Statistics in Medicine, 22, 1365-1381. http://dx.doi.org/10.1002/sim.1501
    • (2003) Statistics in Medicine , vol.22 , pp. 1365-1381
    • Friedman, J.H.1    Meulman, J.J.2
  • 37
    • 84926207567 scopus 로고    scopus 로고
    • Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation
    • Goldstein, A., Kapelner, A., Bleich, J., & Pitkin, E. (2015). Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation. Journal ofComputational and Graphical Statistics, 24, 44-65. http://dx.doi.org/10.1080/10618600.2014.907095
    • (2015) Journal ofComputational and Graphical Statistics , vol.24 , pp. 44-65
    • Goldstein, A.1    Kapelner, A.2    Bleich, J.3    Pitkin, E.4
  • 39
    • 84858711420 scopus 로고    scopus 로고
    • Regularization for generalized additive mixed models by likelihood-based boosting
    • Groll, A., & Tutz, G. (2012). Regularization for generalized additive mixed models by likelihood-based boosting. Methods ofInformation in Medicine, 51, 168-177. http://dx.doi.org/10.3414/ME11-02-0021
    • (2012) Methods ofInformation in Medicine , vol.51 , pp. 168-177
    • Groll, A.1    Tutz, G.2
  • 41
    • 0020083498 scopus 로고
    • The meaning and use of the area under a receiver operating characteristic (ROC) curve
    • Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143, 29-36. http://dx.doi.org/10.1148/radiology.143.1.7063747
    • (1982) Radiology , vol.143 , pp. 29-36
    • Hanley, J.A.1    McNeil, B.J.2
  • 44
    • 84946633097 scopus 로고
    • Selection of the best subset in regression analysis
    • Hocking, R. R., & Leslie, R. N. (1967). Selection of the best subset in regression analysis. Technometrics, 9, 531-540.
    • (1967) Technometrics , vol.9 , pp. 531-540
    • Hocking, R.R.1    Leslie, R.N.2
  • 45
    • 84893967115 scopus 로고    scopus 로고
    • Model-based boosting in R: A hands-on tutorial using the R package mboost
    • Hofner, B., Mayr, A., Robinzonov, N., & Schmid, M. (2014). Model-based boosting in R: A hands-on tutorial using the R package mboost. Computational Statistics, 29, 3-35. http://dx.doi.org/10.1007/s00180-012-0382-5
    • (2014) Computational Statistics , vol.29 , pp. 3-35
    • Hofner, B.1    Mayr, A.2    Robinzonov, N.3    Schmid, M.4
  • 51
    • 33751543346 scopus 로고    scopus 로고
    • Splitting variable selection for multivariate regression trees
    • Hsiao, W. C., & Shih, Y. S. (2007). Splitting variable selection for multivariate regression trees. Statistics & Probability Letters, 77, 265-271. http://dx.doi.org/10.1016/j.spl.2006.08.014
    • (2007) Statistics & Probability Letters , vol.77 , pp. 265-271
    • Hsiao, W.C.1    Shih, Y.S.2
  • 52
    • 77951653583 scopus 로고    scopus 로고
    • Erratum to "An extension of dominance analysis to canonical correlation analysis."
    • Huo, Y., & Budescu, D. V. (2009). Erratum to "An extension of dominance analysis to canonical correlation analysis." Multivariate Behavioral Research 44, 859. http://dx.doi.org/10.1080/00273170903467679
    • (2009) Multivariate Behavioral Research , vol.44 , pp. 859
    • Huo, Y.1    Budescu, D.V.2
  • 53
    • 0001815269 scopus 로고
    • Constructing optimal binary decision trees is NP-complete
    • Hyafil, L., & Rivest, R. L. (1976). Constructing optimal binary decision trees is NP-complete. Information Processing Letters, 5, 15-17. http://dx.doi.org/10.1016/0020-0190(76)90095-8
    • (1976) Information Processing Letters , vol.5 , pp. 15-17
    • Hyafil, L.1    Rivest, R.L.2
  • 54
    • 0014129195 scopus 로고
    • Hierarchical clustering schemes
    • Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32, 241-254. http://dx.doi.org/10.1007/BF02289588
    • (1967) Psychometrika , vol.32 , pp. 241-254
    • Johnson, S.C.1
  • 55
    • 0041625403 scopus 로고
    • Redundancy analysis: An alternative to canonical correlation and multivariate multiple regression in exploring interset associations
    • Lambert, Z. V., Wildt, A. R., & Durand, R. M. (1988). Redundancy analysis: An alternative to canonical correlation and multivariate multiple regression in exploring interset associations. Psychological Bulletin, 104, 282-289. http://dx.doi.org/10.1037/0033-2909.104.2.282
    • (1988) Psychological Bulletin , vol.104 , pp. 282-289
    • Lambert, Z.V.1    Wildt, A.R.2    Durand, R.M.3
  • 56
    • 84876056969 scopus 로고    scopus 로고
    • Regression trees for longitudinal and multiresponse data
    • Loh, W. Y., & Zheng, W. (2013). Regression trees for longitudinal and multiresponse data. The Annals of Applied Statistics, 7, 495-522. http://dx.doi.org/10.1214/12-AOAS596
    • (2013) The Annals of Applied Statistics , vol.7 , pp. 495-522
    • Loh, W.Y.1    Zheng, W.2
  • 57
    • 33746152094 scopus 로고    scopus 로고
    • Boosting for high-multivariate responses in high-dimensional linear regression
    • Lutz, R. W., & Bůhlmann, P. (2006). Boosting for high-multivariate responses in high-dimensional linear regression. Statistica Sinica, 16, 471-494.
    • (2006) Statistica Sinica , vol.16 , pp. 471-494
    • Lutz, R.W.1    Bůhlmann, P.2
  • 58
    • 39549113455 scopus 로고    scopus 로고
    • Classification trees distinguish suicide attempters in major psychiatric disorders: A model of clinical decision making
    • Mann, J. J., Ellis, S. P., Waternaux, C. M., Liu, X., Oquendo, M. A., Malone, K. M., Currier, D. (2008). Classification trees distinguish suicide attempters in major psychiatric disorders: A model of clinical decision making. The Journal of Clinical Psychiatry, 69, 23-31. http://dx.doi.org/10.4088/JCP.v69n0104
    • (2008) The Journal of Clinical Psychiatry , vol.69 , pp. 23-31
    • Mann, J.J.1    Ellis, S.P.2    Waternaux, C.M.3    Liu, X.4    Oquendo, M.A.5    Malone, K.M.6    Currier, D.7
  • 59
    • 84864621380 scopus 로고    scopus 로고
    • Trending: The promises and the challenges of big social data
    • M. K. Gold (Ed.), Minneapolis, MN: University of Minnesota Press
    • Manovich, L. (2012). Trending: The promises and the challenges of big social data. In M. K. Gold (Ed.), Debates in the digital humanities (pp. 460-475). Minneapolis, MN: University of Minnesota Press. http://dx.doi.org/10.5749/minnesota/9780816677948.003.0047
    • (2012) Debates in the digital humanities , pp. 460-475
    • Manovich, L.1
  • 60
    • 85003910494 scopus 로고    scopus 로고
    • Ensemble trees and CLTs: Statistical inference for supervised learning
    • Mentch, L., & Hooker, G. (2014). Ensemble trees and CLTs: Statistical inference for supervised learning. arXiv:1404.6473.
    • (2014) ArXiv:1404.6473
    • Mentch, L.1    Hooker, G.2
  • 62
    • 77955857683 scopus 로고    scopus 로고
    • Revisiting interpretation of canonical correlation analysis: A tutorial and demonstration of canonical commonality analysis
    • Nimon, K., Henson, R. K., & Gates, M. S. (2010). Revisiting interpretation of canonical correlation analysis: A tutorial and demonstration of canonical commonality analysis. Multivariate Behavioral Research, 45, 702-724. http://dx.doi.org/10.1080/00273171.2010.498293
    • (2010) Multivariate Behavioral Research , vol.45 , pp. 702-724
    • Nimon, K.1    Henson, R.K.2    Gates, M.S.3
  • 64
    • 0033826748 scopus 로고    scopus 로고
    • Development and validation of a scale to measure perceived control of internal states
    • Pallant, J. F. (2000). Development and validation of a scale to measure perceived control of internal states. Journal ofPersonality Assessment, 75, 308-337. http://dx.doi.org/10.1207/S15327752JPA7502_10
    • (2000) Journal ofPersonality Assessment , vol.75 , pp. 308-337
    • Pallant, J.F.1
  • 66
    • 21844512391 scopus 로고
    • Large sample confidence regions based on subsamples under minimal assumptions
    • Politis, D. N., & Romano, J. P. (1994). Large sample confidence regions based on subsamples under minimal assumptions. Annals of Statistics, 22, 2031-2050. http://dx.doi.org/10.1214/aos/1176325770
    • (1994) Annals of Statistics , vol.22 , pp. 2031-2050
    • Politis, D.N.1    Romano, J.P.2
  • 67
    • 0020710183 scopus 로고
    • Measures of perceived social support from friends and from family: Three validation studies
    • Procidano, M. E., & Heller, K. (1983). Measures of perceived social support from friends and from family: Three validation studies. American Journal ofCommunity Psychology, 11, 1-24. http://dx.doi.org/10.1007/BF00898416
    • (1983) American Journal ofCommunity Psychology , vol.11 , pp. 1-24
    • Procidano, M.E.1    Heller, K.2
  • 70
    • 0000230432 scopus 로고
    • The desired control measure and adjustment among the elderly
    • H. M. Lefcourt (Ed.), New York, NY: Academic Press
    • Reid, D. W., & Ziegler, M. (1981). The desired control measure and adjustment among the elderly. In H. M. Lefcourt (Ed.), Research with the locus ofcontrol construct: Vol. 1, Assessment methods (pp. 127-159). New York, NY: Academic Press. http://dx.doi.org/10.1016/B978-0-12-443201-7.50008-7
    • (1981) Research with the locus ofcontrol construct: Vol. 1, Assessment methods , pp. 127-159
    • Reid, D.W.1    Ziegler, M.2
  • 73
    • 84903132756 scopus 로고    scopus 로고
    • Boosting techniques for nonlinear time series models
    • Robinzonov, N., Tutz, G., & Hothorn, T. (2012). Boosting techniques for nonlinear time series models. Advances in Statistical Analysis, 96, 99-122. http://dx.doi.org/10.1007/s10182-011-0163-4
    • (2012) Advances in Statistical Analysis , vol.96 , pp. 99-122
    • Robinzonov, N.1    Tutz, G.2    Hothorn, T.3
  • 74
    • 85047686139 scopus 로고
    • The revised UCLA Loneliness Scale: Concurrent and discriminant validity evidence
    • Russell, D., Peplau, L. A., & Cutrona, C. E. (1980). The revised UCLA Loneliness Scale: Concurrent and discriminant validity evidence. Journal of Personality and Social Psychology, 39, 472-480. http://dx.doi.org/10.1037/0022-3514.39.3.472
    • (1980) Journal of Personality and Social Psychology , vol.39 , pp. 472-480
    • Russell, D.1    Peplau, L.A.2    Cutrona, C.E.3
  • 75
    • 0029392602 scopus 로고
    • The structure of psychological well-being revisited
    • Ryff, C. D., & Keyes, C. L. M. (1995). The structure of psychological well-being revisited. Journal ofPersonality and Social Psychology, 69, 719-727. http://dx.doi.org/10.1037/0022-3514.69.4.719
    • (1995) Journal ofPersonality and Social Psychology , vol.69 , pp. 719-727
    • Ryff, C.D.1    Keyes, C.L.M.2
  • 76
    • 82955190516 scopus 로고    scopus 로고
    • What contributes to perceived stress in later life? A recursive partitioning approach
    • Scott, S. B., Jackson, B. R., & Bergeman, C. S. (2011). What contributes to perceived stress in later life? A recursive partitioning approach. Psychology and Aging, 26, 830-843. http://dx.doi.org/10.1037/a0023180
    • (2011) Psychology and Aging , vol.26 , pp. 830-843
    • Scott, S.B.1    Jackson, B.R.2    Bergeman, C.S.3
  • 78
    • 0000644048 scopus 로고
    • Tree-structured methods for longitudinal data
    • Segal, M. R. (1992). Tree-structured methods for longitudinal data. Journal ofthe American Statistical Association, 87, 407-418. http://dx.doi.org/10.1080/01621459.1992.10475220
    • (1992) Journal ofthe American Statistical Association , vol.87 , pp. 407-418
    • Segal, M.R.1
  • 80
    • 84868300223 scopus 로고    scopus 로고
    • RE-EM trees: A data mining approach for longitudinal and clustered data
    • Sela, R. J., & Simonoff, J. S. (2012). RE-EM trees: A data mining approach for longitudinal and clustered data. Machine Learning, 86, 169-207. http://dx.doi.org/10.1007/s10994-011-5258-3
    • (2012) Machine Learning , vol.86 , pp. 169-207
    • Sela, R.J.1    Simonoff, J.S.2
  • 81
    • 0030720998 scopus 로고    scopus 로고
    • Cumulative and compensatory effects of competence and incompetence on depressive symptoms in children
    • Seroczynski, A. D., Cole, D. A., & Maxwell, S. E. (1997). Cumulative and compensatory effects of competence and incompetence on depressive symptoms in children. Journal of Abnormal Psychology, 106, 586-597. http://dx.doi.org/10.1037/0021-843X.106.4.586
    • (1997) Journal of Abnormal Psychology , vol.106 , pp. 586-597
    • Seroczynski, A.D.1    Cole, D.A.2    Maxwell, S.E.3
  • 83
    • 61849178068 scopus 로고    scopus 로고
    • Boosting nonlinear additive autoregressive time series
    • Shafik, N., & Tutz, G. (2009). Boosting nonlinear additive autoregressive time series. Computational Statistics & Data Analysis, 53, 2453-2464. http://dx.doi.org/10.1016/j.csda.2008.12.006
    • (2009) Computational Statistics & Data Analysis , vol.53 , pp. 2453-2464
    • Shafik, N.1    Tutz, G.2
  • 84
    • 33847096395 scopus 로고    scopus 로고
    • Bias in random forest variable importance measures: Illustrations, sources and a solution
    • Strobl, C., Boulesteix, A.-L., Zeileis, A., & Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics, 8, 25. http://dx.doi.org/10.1186/1471-2105-8-25
    • (2007) BMC Bioinformatics , vol.8 , pp. 25
    • Strobl, C.1    Boulesteix, A.-L.2    Zeileis, A.3    Hothorn, T.4
  • 85
    • 72449170109 scopus 로고    scopus 로고
    • An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests
    • Strobl, C., Malley, J., & Tutz, G. (2009). An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological Methods, 14, 323-348. http://dx.doi.org/10.1037/a0016973
    • (2009) Psychological Methods , vol.14 , pp. 323-348
    • Strobl, C.1    Malley, J.2    Tutz, G.3
  • 86
    • 33745775676 scopus 로고    scopus 로고
    • Constraint based induction of multi-objective regression trees
    • F. Bonchi & J. F. Boulicaut (Eds.), Berlin, Germany: Springer
    • Struyf, J., & Dzeroski, S. (2006). Constraint based induction of multi-objective regression trees. In F. Bonchi & J. F. Boulicaut (Eds.), Knowledge discovery in inductive databases (Vol. 3933, pp. 222-233). Berlin, Germany: Springer. http://dx.doi.org/10.1007/11733492_13
    • (2006) Knowledge discovery in inductive databases , vol.3933 , pp. 222-233
    • Struyf, J.1    Dzeroski, S.2
  • 87
    • 84950758368 scopus 로고
    • The calculation of posterior distributions by data augmentation
    • Tanner, M. A., & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal ofthe American Statistical Association, 82, 528-540. http://dx.doi.org/10.1080/01621459.1987.10478458
    • (1987) Journal ofthe American Statistical Association , vol.82 , pp. 528-540
    • Tanner, M.A.1    Wong, W.H.2
  • 88
    • 84973777204 scopus 로고
    • Stepwise regression and stepwise discriminant analysis need not apply
    • Thompson, B. (1995). Stepwise regression and stepwise discriminant analysis need not apply. Educational and Psychological Measurement, 55, 524-534.
    • (1995) Educational and Psychological Measurement , vol.55 , pp. 524-534
    • Thompson, B.1
  • 89
    • 84894807965 scopus 로고    scopus 로고
    • Canonical correlation analysis
    • B. S. Everitt & D. Howell (Eds.), Hoboken, NJ: Wiley, Ltd
    • Thompson, B. (2005). Canonical correlation analysis. In B. S. Everitt & D. Howell (Eds.), Encyclopedia ofstatistics in behavioral science. Hoboken, NJ: Wiley, Ltd. http://dx.doi.org/10.1002/0470013192.bsa068
    • (2005) Encyclopedia ofstatistics in behavioral science
    • Thompson, B.1
  • 92
    • 84873489284 scopus 로고    scopus 로고
    • TATES: Efficient multivariate genotype-phenotype analysis for genome-wide association studies
    • Van der Sluis, S., Posthuma, D., & Dolan, C. V. (2013). TATES: Efficient multivariate genotype-phenotype analysis for genome-wide association studies. PLOS Genetics, 9, e1003235. http://dx.doi.org/10.1371/journal.pgen.1003235
    • (2013) PLOS Genetics , vol.9
    • Van der Sluis, S.1    Posthuma, D.2    Dolan, C.V.3
  • 93
    • 27544465085 scopus 로고    scopus 로고
    • Predicting well-being outcomes in later life: An application of classification and regression tree (CART) analysis
    • S. P. Shohov (Ed.), Hauppauge, NY: Nova Science Publishers
    • Wallace, K. A., Bergeman, C. S., & Maxwell, S. E. (2002). Predicting well-being outcomes in later life: An application of classification and regression tree (CART) analysis. In S. P. Shohov (Ed.), Advances in psychology research (Vol. 17, pp. 71-92). Hauppauge, NY: Nova Science Publishers.
    • (2002) Advances in psychology research , vol.17 , pp. 71-92
    • Wallace, K.A.1    Bergeman, C.S.2    Maxwell, S.E.3
  • 94
    • 70450188142 scopus 로고    scopus 로고
    • Boosted multi-task learning for face verification with applications to web image and video search
    • Washington, DC: IEEE
    • Wang, X., Zhang, C., & Zhang, Z. (2009). Boosted multi-task learning for face verification with applications to web image and video search. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (pp. 142-149). Washington, DC: IEEE.
    • (2009) Proceedings IEEE Conference on Computer Vision and Pattern Recognition , pp. 142-149
    • Wang, X.1    Zhang, C.2    Zhang, Z.3


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