504
Views
4
CrossRef citations to date
0
Altmetric
Articles

Performance of asymmetric links and correction methods for imbalanced data in binary regression

, ORCID Icon, & ORCID Icon
Pages 1694-1714 | Received 09 Aug 2018, Accepted 08 Mar 2019, Published online: 27 Mar 2019

References

  • Collett D. Modelling binary data. 2nd ed. Boca Raton (FL): CRC Press; 2002.
  • Qiu Z, Li H, Su H, et al. Logistic regression bias correction for large scale data with rare events. In: International Conference on Advanced Data Mining and Applications; Springer; 2013. p. 133–144.
  • Czado C, Santner TJ. The effect of link misspecification on binary regression inference. J Stat Plan Inference. 1992;33(2):213–231. doi: 10.1016/0378-3758(92)90069-5
  • Van der Paal B. A comparison of different methods for modelling rare events data [master's thesis]. Ghent University; 2014.
  • King G, Zeng L. Logistic regression in rare events data. Polit Anal. 2001;9(2):137–163. doi: 10.1093/oxfordjournals.pan.a004868
  • Firth D. Bias reduction of maximum likelihood estimates. Biometrika. 1993;80(1):27–38. doi: 10.1093/biomet/80.1.27
  • Chen MH, Dey DK, Shao QM. A new skewed link model for dichotomous quantal response data. J Am Stat Assoc. 1999;94(448):1172–1186. doi: 10.1080/01621459.1999.10473872
  • Prentice RL. A generalization of the probit and logit methods for dose response curves. Biometrics. 1976;32(4):761–768. doi: 10.2307/2529262
  • Kim S, Chen MH, Dey DK. Flexible generalized t-link models for binary response data. Biometrika. 2007;95(1):93–106. doi: 10.1093/biomet/asm079
  • Wang X, Dey DK. Generalized extreme value regression for binary response data: an application to b2b electronic payments system adoption. Ann Appl Stat. 2010;4(4):2000–2023. doi: 10.1214/10-AOAS354
  • Bazán J, Romeo J, Rodrigues J. Bayesian skew-probit regression for binary response data. Braz J Probab Stat. 2014;28(4):467–482. doi: 10.1214/13-BJPS218
  • Lemonte AJ, Bazán JL. New links for binary regression: an application to coca cultivation in peru. TEST. 2018;27(3):597–617. doi: 10.1007/s11749-017-0563-1
  • Bazán J, Torres-Avilés F, Suzuki A, et al. Power and reversal power links for binary regressions: an application for motor insurance policyholders. Appl Stoch Models Bus Ind. 2017;33(1):22–34. doi: 10.1002/asmb.2215
  • Hoffman MD, Gelman A. The no-u-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J Mach Learn Res. 2014;15(1):1593–1623.
  • Heinze G, Ploner M, Dunkler D, et al. Firths bias reduced logistic regression. R package version. 2013;1.
  • Heinze G, Schemper M. A solution to the problem of separation in logistic regression. Stat Med. 2002;21(16):2409–2419. doi: 10.1002/sim.1047
  • Choirat C, Honaker J, Imai K, et al. Zelig: everyone's statistical software; 2018. Version 5.1.6; Available from: http://zeligproject.org/.
  • Kosuke I, King G, Lau O. Relogit: rare events logistic regression for dichotomous dependent variables. Zelig: everyones statistical software. 2007.
  • Carpenter B, Gelman A, Hoffman MD. Stan: a probabilistic programming language. J Stat Softw. 2017;76(1):1–32. doi: 10.18637/jss.v076.i01
  • Team SD. Pystan: the python interface to stan, version 2.16.0.0.; 2017. Available from: http://mc-stan.org.
  • Watanabe S. Asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory. J Mach Learn Res. 2010;11:3571–3594.
  • Geisser S, Eddy WF. A predictive approach to model selection. J Am Stat Assoc. 1979;74(365):153–160. doi: 10.1080/01621459.1979.10481632
  • Vehtari A, Gelman A, Gabry J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat Comput. 2017;27(5):1413–1432. doi: 10.1007/s11222-016-9696-4
  • Dunn PK, Smyth GK. Randomized quantile residuals. J Comput Graph Stat. 1996;5(3): 236–244.
  • Atkinson A. Plots, transformations, and regression: an introduction to graphical methods of diagnostic regression analysis. Oxford: Clarendon Press; 1985.
  • Cortez P, Cerdeira A, Almeida F, et al. Modeling wine preferences by data mining from physicochemical properties. Decis Support Syst. 2009;47(4):547–553. doi: 10.1016/j.dss.2009.05.016
  • Dua D, Karra Taniskidou E. UCI machine learning repository; 2017. Available from: http://archive.ics.uci.edu/ml.
  • Lemionet A, Liu Y, Zhou Z. Predicting quality of wine based on chemical attributes. CS 229 project. 2015; Available from: http://cs229.stanford.edu/proj2015/245_report.pdf.
  • Ding P. Bayesian robust inference of sample selection using selection-t models. J Multivar Anal. 2014;124:451–464. doi: 10.1016/j.jmva.2013.11.014
  • Fawcett T. An introduction to roc analysis. Pattern Recognit Lett. 2006;27(8):861–874. doi: 10.1016/j.patrec.2005.10.010
  • Choi SS, Cha SH, Tappert CC. A survey of binary similarity and distance measures. J Syst Cybern Inf. 2010;8(1):43–48.
  • Schaefer JT. The critical success index as an indicator of warning skill. Weather Forecasting. 1990;5(4):570–575. doi: 10.1175/1520-0434(1990)005<0570:TCSIAA>2.0.CO;2
  • Kim S, Chen MH, Dey DK. Flexible generalized t-link models for binary response data. Biometrika. 2008;95(1):93–106. doi: 10.1093/biomet/asm079
  • Bolfarine H, Bazan JL. Bayesian estimation of the logistic positive exponent irt model. J Educ Behav Stat. 2010;35(6):693–713. doi: 10.3102/1076998610375834

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.