Skip to main content

Discussion of “Akaike Memorial Lecture 2020: Some of the challenges of statistical applications”

This is a preview of subscription content, access via your institution.

Fig. 1

References

  • Copas, J. B. (1983). Regression, prediction and shrinkage. Journal of the Royal Statistical Society, Series B, 45, 311–335.

    MathSciNet  MATH  Google Scholar 

  • Copas, J. B., Eguchi, S. (2010). Likelihood for statistically equivalent models. Journal of the Royal Statistical Society, Series B, 72, 193–217.

    MathSciNet  Article  Google Scholar 

  • Copas, J. B., Li, H. G. (1997). Inference for non-random samples (with discussion). Journal of the Royal Statistical Society, Series B, 59, 55–95.

    MathSciNet  Article  Google Scholar 

  • Efron, B., Hastie, T. (2016). Computer age statistical inference. Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • Greenland, S. (2000). When should epidemiologic regressions use random coefficients? Biometrics, 56, 915–921.

    Article  Google Scholar 

  • Greenland, S., Lash, T. L. (2008). Bias Analysis. In K. J. Rothman, S. Greenland, T. L. Lash (Eds.), Modern epidemiology, 3rd ed. (pp. 345–380). Philadelphia: Lippincott-Williams-Wilkins.

    Google Scholar 

  • Harrell, F. E., Jr. (2015). Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis, 2nd ed. New York: Springer.

    Book  Google Scholar 

  • Houwelingen, J. C., Le Cessie, S. (1990). Predictive value of statistical models. Statistics in Medicine, 9, 1303–1325.

    Article  Google Scholar 

  • Huang, H. (2017). Controlling the false discoveries in LASSO. Biometrics, 73, 1102–1110.

    MathSciNet  Article  Google Scholar 

  • Koch, B., Vock, D. M., Wolfson, J. (2018). Covariate selection with group lasso and doubly robust estimation of causal effects. Biometrics, 74, 8–17.

    MathSciNet  Article  Google Scholar 

  • Taguri, M., Chiba, Y. (2012). Instruments and bounds for causal effects under the monotonic selection assumption. The International Journal of Biostatistics, 8(1), 24.

    MathSciNet  Article  Google Scholar 

  • Taguri, M., Chiba, Y. (2015). A principal stratification approach for evaluating natural direct and indirect effects in the presence of treatment-induced intermediate confounding. Statistics in Medicine, 34, 131–144.

    MathSciNet  Article  Google Scholar 

  • Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B, 58, 267–288.

    MathSciNet  MATH  Google Scholar 

  • van der Laan, M. J., Rose, S. (2011). Targeted learning: Causal inference for observational and experimental data. New York: Springer.

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Masataka Taguri.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Verify currency and authenticity via CrossMark

Cite this article

Taguri, M. Discussion of “Akaike Memorial Lecture 2020: Some of the challenges of statistical applications”. Ann Inst Stat Math 74, 643–647 (2022). https://doi.org/10.1007/s10463-022-00829-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10463-022-00829-3

Keywords

  • Statistical science
  • Shrinkage
  • Selection bias
  • Likelihood
  • Publication bias