Step away from stepwise

Efroymson MA. Multiple regression analysis. In: Ralston A, Wilf HS, editors. Mathematical methods for digital computers. New York: Wiley; 1960. Thompson B. Why won’t stepwise methods die? Meas Eval Couns Dev. 1989;21(4):146–8. Hurvich CM, Tsai CL. The impact of model selection on inference in linear regression. Am Stat. 1990;44(3):214–7. Harrell FE Jr. Regression modeling strategies: with applications to linear models, logistic regression and survival analysis. New York: Springer; 2001. Hendry DF, Krolzig HM. Automatic econometric model selection. London: Timberlake Consultants Press; 2001. Babyak MA. What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom Med. 2004;66:411–21. Whittingham MJ, Stephens PA, Bradbury RB, Freckleton RP. Why do we still use stepwise modelling in ecology and behaviour? J Anim Ecol. 2006;75(5):1182–9. Castle JL, Fawcett NWP, Hendry DF. Evaluating automatic model selection, Technical Report 474. Oxford: Department of Economics, University of Oxford; 2010. Flom PL, Cassell DL. Stopping stepwise: why stepwise and similar selection methods are bad, and what you should use. In: NESUG 2007 proceedings. 2007. Thompson B. Stepwise regression and stepwise discriminant analysis need not apply here: a guidelines editorial. Educ Psychol Meas. 1995;55:525–34. Marascuilo LA, Serlin RC. Statistical methods for thesocial and behavioral sciences. New York: W. H. Freeman; 1988. Huberty CJ. Problems with stepwise methods—better alternatives. In: Thompson B, editor. Advances in social science methodology, vol. 1. Greenwich: JAI Press; 1989. Vlachopoulou M, Ferryman TA, Zhou N, Tong J. A stepwise regression method for forecasting net interchange schedule. https://doi.org/10.1109/pesmg.2013.6672763. 2013. Walter S, Tiemeier H. Variable selection: current practice in epidemiological studies. Eur J Epidemiol. 2009;24(12):733–6. Liao H, Lynn HS. A survey of variable selection methods in two Chinese epidemiology journals. BMC Med Res Methodol. 2010;10:87. https://doi.org/10.1186/1471-2288-10-87. Rachev ST, Mittnik S, Fabozzi FJ, Focardi SM, Jašić T. Financial econometrics: from basics to advanced modeling techniques. New York: Wiley; 2006. McDonald JH. Handbook of biological statistics. 3rd ed. Baltimore: Sparky House Publishing; 2014. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. 2nd ed. New York: Springer; 2016. Wiley. Wiley 11th hour study guide for level II CFA exam. 2nd ed. New York: Wiley; 2017. p. 31. Friedman M. The permanent income hypothesis: a theory of the consumption function. Princeton: Princeton University Press; 1957. Fayyad U, Piatetsky-Shapiro G, Smyth P. From data mining to knowledge discovery in databases. AI Mag. 1996;17(3):37–54. Kecman V. Foreword. In: Cios KJ, Pedrycz W, Swiniarski RW, Kurgan LA, editors. Data mining: a knowledge discovery approach. New York: Springer; 2007. Begoli E, Horsey J. Design principles for effective knowledge discovery from big data. In: Software architecture (WICSA) and European conference on software architecture (ECSA), 2012 joint working IEEE/IFIP conference. Piatetsky-Shapiro G. Knowledge discovery in real databases: a report on the IJCAI-89 workshop. AI Mag. 1991;11(5):68–70. Sagiroglu S, Sinanc D. Big data: a review. In: 2013 international conference on collaboration technologies and systems (CTS). 2013. Kecman V. Foreword. In: Cios KJ, Pedrycz W, Swiniarski RW, Kurgan LA. Data mining: a knowledge discovery approach. New York: Springer; 2007. Tullock G. A comment on Daniel Klein’s “A plea to economists who favor liberty”. East Econ J. 2001;27(2):203–7. Wooldridge JW. Introductory econometrics: a modern approach. 3rd ed. Mason: Thompson; 2006. p. 94–7. Stock JH, Watson MW. Introduction to econometrics. 2nd ed. Boston: Pearson; 2007. p. 316–9. Hastie T, Tibshirani R, Friedman J. the elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York: Springer; 2009. http://www-stat.stanford.edu/~tibs/ElemStatLearn/download.html. Varian HR. Big data: new tricks for econometrics. J Econ Perspect. 2014;28(2):3–27. Bruce P, Bruce A. Practical statistics for data scientists: 50 essential concepts. Sebastopol: O’Reilly Media; 2017. Calude CS, Longo G. The deluge of spurious correlations in big data. Found Sci. 2016. https://doi.org/10.1007/s10699-016-9489-4. Steyerberg EW, Eijkemans MJC, Habbema JDF. Stepwise selection in small data sets: a simulation study of bias in logistic regression analysis. J Clin Epidemiol. 1999;52(10):935–42. Derksen S, Keselman HJ. Backward, forward and stepwise automated subset selection algorithms: frequency of obtaining authentic and noise variables. Br J Math Stat Psychol. 1992;45(2):265–82. Mayers JH, Forgy EW. The development of numerical credit evaluation systems. J Am Stat Assoc. 1963;58(303):799–806. Mark J, Goldberg MA. Multiple regression analysis and mass assessment: a review of the issues. Apprais J. 2001;56:89–109. Guyan I, Weston J, Barnhill S, Vopnik V. Gene selection for cancer classification using support vector machines. Mach Learn. 2002;46:389–422. Mukherjee T, Duckat M, Kumar P, Paquet JD, Rodriguez D, Haulcomb M, George K, Pasiliao E. RSSI-based supervised learning for uncooperative direction-finding. In: Altun Y, editor. Machine learning and knowledge discovery in databases. ECML PKDD 2017, vol. 10536., Lecture Notes in ComputerCham: Springer; 2015. Deng H, Runger G. Feature selection via regularized trees. In: Proceedings of the 2012 international joint conference on neural networks (IJCNN), IEEE; 2012. Box GEP, Tiao GC. Bayesian inference in statistical analysis. New York: Wiley; 1973. Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian data analysis. 2nd ed. Boca Raton: Chapman and Hall/CRC; 2003. Koehrsen W. Introduction to Bayesian linear regression. Towards Data Science. 2018. https://towardsdatascience.com/introduction-to-bayesian-linear-regression-e66e60791ea7. Smith G, Campbell F. A critique of some ridge regression methods. J Am Stat Assoc. 1980;75(369):74–81.