CIVIL ENGINEERING 365 ALL ABOUT CIVIL ENGINEERING


  • 1.

    Leamer, E. E. Let’s take the con out of econometrics. Am. Econ. Rev. 73, 31-43 (1983).

  • 2.

    Ioannidis, J. P. A. Why most published research findings are false. PLoS Med. 2, 696–701 (2005).


    Google Scholar
     

  • 3.

    Simmons, J. P., Nelson, L. D. & Simonsohn, U. False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol. Sci. 22, 1359–1366 (2011).

    Article 

    Google Scholar
     

  • 4.

    Glaeser, E. L. Researcher incentives and empirical methods. NBER Technical Working Paper Series https://doi.org/10.3386/t0329 (2006).

  • 5.

    Efron, B. Estimation and accuracy after model selection. J. Am. Stat. Assoc. 109, 991–1007 (2014).

    CAS 
    Article 

    Google Scholar
     

  • 6.

    White, H. A reality check for data snooping. Econometrica 68, 1097–1126 (2000).

    Article 

    Google Scholar
     

  • 7.

    Athey, S. & Imbens, G. A measure of robustness to misspecification. Am. Econ. Rev. 105, 476–480 (2015).

  • 8.

    Sala-i-Martin, X. X. I just ran two million regressions. Am. Econ. Rev. 87, 178–183 (1997).

  • 9.

    Muñoz, J. & Young, C. We ran 9 billion regressions: eliminating false positives through computational model robustness. Sociol. Methodol. 48, 1–33 (2018).

    Article 

    Google Scholar
     

  • 10.

    Young, C. & Holsteen, K. Model uncertainty and robustness: a computational framework for multimodel analysis. Sociol. Methods Res. 46, 3–40 (2017).

    Article 

    Google Scholar
     

  • 11.

    Miguel, E. et al. Promoting transparency in social science research. Science 343, 30–31 (2014).

    CAS 
    Article 

    Google Scholar
     

  • 12.

    Moore, D. A. Preregister if you want to. Am. Psychol. 71, 238–239 (2016).

    Article 

    Google Scholar
     

  • 13.

    Bhargava, S., Kassam, K. S. & Loewenstein, G. A reassessment of the defense of parenthood. Psychol. Sci. 25, 299–302 (2014).

    Article 

    Google Scholar
     

  • 14.

    DellaVigna, S. & Malmendier, U. Paying not to go to the gym. Am. Econ. Rev. 96, 694–719 (2006).

    Article 

    Google Scholar
     

  • 15.

    Stevenson, B. & Wolfers, J. Economic growth and subjective well-being: reassessing the Easterlin Paradox. Brookings Pap. Econ. Act. 2008, 1–87 (2008).

    Article 

    Google Scholar
     

  • 16.

    Card, D. & Krueger, A. B. Minimum wages and employment: a case study of the fast-food industry in New Jersey and Pennsylvania. Am. Econ. Rev. 84, 772–793 (1994).


    Google Scholar
     

  • 17.

    Jung, K., Shavitt, S., Viswanathan, M. & Hilbe, J. M. Female hurricanes are deadlier than male hurricanes. Proc. Natl Acad. Sci. USA 111, 8782–8787 (2014).

  • 18.

    Malter, D. Female hurricanes are not deadlier than male hurricanes. Proc. Natl Acad. Sci. USA 111, E3496 (2014).

    CAS 
    Article 

    Google Scholar
     

  • 19.

    Maley, S. Statistics show no evidence of gender bias in the public’s hurricane preparedness. Proc. Natl Acad. Sci. USA 111, E3834 (2014).

    CAS 
    Article 

    Google Scholar
     

  • 20.

    Bakkensen, L. & Larson, W. Population matters when modeling hurricane fatalities. Proc. Natl Acad. Sci. USA 111, E5331 (2014).

    CAS 
    Article 

    Google Scholar
     

  • 21.

    Christensen, B. & Christensen, S. Are female hurricanes really deadlier than male hurricanes? Proc. Natl Acad. Sci. USA 111, E3497–E3498 (2014).

    CAS 
    Article 

    Google Scholar
     

  • 22.

    Jung, K., Shavitt, S., Viswanathan, M. & Hilbe, J. M. Reply to Christensen and Christensen and to Malter: pitfalls of erroneous analyses of hurricanes names. Proc. Natl Acad. Sci. USA 111, E3499–E3500 (2014).

    CAS 
    Article 

    Google Scholar
     

  • 23.

    Bertrand, M. & Mullainathan, S. Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. Am. Econ. Rev. 94, 991–1013 (2004).

    Article 

    Google Scholar
     

  • 24.

    Boos, D. D. Introduction to the bootstrap world. Stat. Sci. 18, 168–174 (2003).

    Article 

    Google Scholar
     

  • 25.

    Bickel, P. J. & Ren, J.-J. The bootstrap in hypothesis testing. Proj. Euclid 36, 91–112 (2001).


    Google Scholar
     

  • 26.

    MacKinnon, J. G. in Handbook of Computational Econometrics (eds Belsley, D. A. & Kontoghiorghes, E. J.) 183–213 (Wiley, 2009).

  • 27.

    Paparoditis, E. & Politis, D. N. Bootstrap hypothesis testing in regression models. Stat. Probab. Lett. 74, 356–365 (2005).

    Article 

    Google Scholar
     

  • 28.

    Romano, J. P. Bootstrap and randomization tests of some nonparametric hypotheses. Ann. P Stat. 17, 141–159 (1989).

    Article 

    Google Scholar
     

  • 29.

    Pitman, E. J. G. Significance tests which may be applied to samples from any populations. J. R. Stat. Soc. 4, 119–130 (1937).


    Google Scholar
     

  • 30.

    Fisher, R. A. The Design of Experiments (Oliver and Boyd, 1935).

  • 31.

    Pesarin, F. & Salmaso, L. Permutation Tests for Complex Data: Theory, Applications and Software (John Wiley & Sons, 2010).

  • 32.

    Ernst, M. D. Permutation methods: a basis for exact inference. Stat. Sci. 19, 676–685 (2004).

    Article 

    Google Scholar
     

  • 33.

    Flachaire, E. A better way to bootstrap pairs. Econ. Lett. 64, 257–262 (1999).

    Article 

    Google Scholar
     

  • 34.

    Lancaster, H. Significance tests in discrete distributions. J. Am. Stat. Assoc. 56, 223–234 (1961).

    Article 

    Google Scholar
     



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