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Control of Generalized Error Rates in Multiple Testing

Citations

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Cited by:

  1. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2019. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 749-758, April.
  2. Pallavi Basu & Luella Fu & Alessio Saretto & Wenguang Sun, 2021. "Empirical Bayes Control of the False Discovery Exceedance," Working Papers 2115, Federal Reserve Bank of Dallas.
  3. Fehr, Ernst & Fischbacher, Urs & Kosfeld, Michael, 2005. "Neuroeconomic Foundations of Trust and Social Preferences," IZA Discussion Papers 1641, Institute of Labor Economics (IZA).
  4. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos, 2021. "Trading the foreign exchange market with technical analysis and Bayesian Statistics," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 230-251.
  5. David Afshartous & Michael Wolf, 2007. "Avoiding ‘data snooping’ in multilevel and mixed effects models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 1035-1059, October.
  6. Heath, Davidson & Ringgenberg, Matthew C. & Samadi, Mehrdad & Werner, Ingrid M., 2019. "Reusing Natural Experiments," Working Paper Series 2019-21, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
  7. Hsu, Po-Hsuan & Han, Qiheng & Wu, Wensheng & Cao, Zhiguang, 2018. "Asset allocation strategies, data snooping, and the 1 / N rule," Journal of Banking & Finance, Elsevier, vol. 97(C), pages 257-269.
  8. Joseph Romano & Azeem Shaikh & Michael Wolf, 2008. "Control of the false discovery rate under dependence using the bootstrap and subsampling," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(3), pages 417-442, November.
  9. Wang, Li & Xu, Xingzhong, 2012. "Step-up procedure controlling generalized family-wise error rate," Statistics & Probability Letters, Elsevier, vol. 82(4), pages 775-782.
  10. Hassler Uwe & Werkmann Verena, 2014. "Multiple Comparisons and Joint Significance in Panel Unit Root Testing with Evidence on International Interest Rate Linkage," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 234(1), pages 23-43, February.
  11. Kearney, Fearghal & Cummins, Mark & Murphy, Finbarr, 2014. "Outperformance in exchange-traded fund pricing deviations: Generalized control of data snooping bias," Journal of Financial Markets, Elsevier, vol. 19(C), pages 86-109.
  12. Romano, Joseph P. & Shaikh, Azeem M. & Wolf, Michael, 2008. "Formalized Data Snooping Based On Generalized Error Rates," Econometric Theory, Cambridge University Press, vol. 24(2), pages 404-447, April.
  13. Joseph P. Romano & Azeem M. Shaikh & Michael Wolf, 2010. "Hypothesis Testing in Econometrics," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 75-104, September.
  14. Cummins, Mark, 2013. "EU ETS market interactions: The case for multiple hypothesis testing approaches," Applied Energy, Elsevier, vol. 111(C), pages 701-709.
  15. Stephen A. Gorman & Frank J. Fabozzi, 2021. "The ABC’s of the alternative risk premium: academic roots," Journal of Asset Management, Palgrave Macmillan, vol. 22(6), pages 405-436, October.
  16. Youwei Wang & Yuxin Chen & Yi Qian, 2018. "The Causal Link between Relative Age Effect and Entrepreneurship: Evidence from 17 Million Users across 49 Years on Taobao," NBER Working Papers 25318, National Bureau of Economic Research, Inc.
  17. Dichtl, Hubert & Drobetz, Wolfgang & Neuhierl, Andreas & Wendt, Viktoria-Sophie, 2021. "Data snooping in equity premium prediction," International Journal of Forecasting, Elsevier, vol. 37(1), pages 72-94.
  18. Cummins, Mark & Garry, Oonagh & Kearney, Claire, 2014. "Price discovery analysis of green equity indices using robust asymmetric vector autoregression," International Review of Financial Analysis, Elsevier, vol. 35(C), pages 261-267.
  19. Mathur, Maya B & VanderWeele, Tyler, 2018. "Statistical methods for evidence synthesis," Thesis Commons kd6ja, Center for Open Science.
  20. Liao Zhu, 2021. "The Adaptive Multi-Factor Model and the Financial Market," Papers 2107.14410, arXiv.org, revised Aug 2021.
  21. Armin Falk & Ernst Fehr & Christian Zehnder, "undated". "The Behavioral Effects of Minimum Wages," IEW - Working Papers 247, Institute for Empirical Research in Economics - University of Zurich.
  22. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Paul Doukhan & Gabriel Lang & Anne Leucht & Michael H. Neumann, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 290-314, May.
  23. Jin, Xiaoye, 2022. "Performance of intraday technical trading in China’s gold market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 76(C).
  24. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Robust inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers 70/13, Institute for Fiscal Studies.
  25. Joseph P. Romano & Michael Wolf, 2008. "Balanced Control of Generalized Error Rates," IEW - Working Papers 379, Institute for Empirical Research in Economics - University of Zurich.
  26. Tania Singer & Ernst Fehr, 2005. "The Neuroeconomics of Mind Reading and Empathy," American Economic Review, American Economic Association, vol. 95(2), pages 340-345, May.
  27. Anatolyev Stanislav & Kosenok Grigory, 2018. "Sequential Testing with Uniformly Distributed Size," Journal of Time Series Econometrics, De Gruyter, vol. 10(2), pages 1-22, July.
  28. Krauss, Christopher, 2015. "Statistical arbitrage pairs trading strategies: Review and outlook," FAU Discussion Papers in Economics 09/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
  29. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
  30. Georgios Sermpinis & Andreas Karathanasopoulos & Rafael Rosillo & David Fuente, 2021. "Neural networks in financial trading," Annals of Operations Research, Springer, vol. 297(1), pages 293-308, February.
  31. Michael Wolf & Dan Wunderli, 2012. "Bootstrap joint prediction regions," ECON - Working Papers 064, Department of Economics - University of Zurich, revised May 2013.
  32. Vincent, Kendro & Hsu, Yu-Chin & Lin, Hsiou-Wei, 2021. "Investment styles and the multiple testing of cross-sectional stock return predictability," Journal of Financial Markets, Elsevier, vol. 56(C).
  33. Li Wang & Xingzhong Xu & Yong A, 2016. "New multiple testing method under no dependency assumption, with application to multiple comparisons problem," Statistical Papers, Springer, vol. 57(1), pages 161-183, March.
  34. Yu-Chin Hsu & Hsiou-Wei Lin & Kendro Vincent, 2017. "Do Cross-Sectional Stock Return Predictors Pass the Test without Data-Snooping Bias?," IEAS Working Paper : academic research 17-A003, Institute of Economics, Academia Sinica, Taipei, Taiwan.
  35. Yang, Junmin & Cao, Zhiguang & Han, Qiheng & Wang, Qiyu, 2019. "Tactical asset allocation on technical trading rules and data snooping," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
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