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Generalized Maximally Selected Statistics

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  • Torsten Hothorn
  • Achim Zeileis

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  • Torsten Hothorn & Achim Zeileis, 2008. "Generalized Maximally Selected Statistics," Biometrics, The International Biometric Society, vol. 64(4), pages 1263-1269, December.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:4:p:1263-1269
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2008.00995.x
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    References listed on IDEAS

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    1. Hansen, Bruce E, 1997. "Approximate Asymptotic P Values for Structural-Change Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 60-67, January.
    2. Lausen, Berthold & Schumacher, Martin, 1996. "Evaluating the effect of optimized cutoff values in the assessment of prognostic factors," Computational Statistics & Data Analysis, Elsevier, vol. 21(3), pages 307-326, March.
    3. Miwa, Tetsuhisa & Hayter, A. J. & Liu, Wei, 2000. "Calculations of level probabilities for normal random variables with unequal variances with applications to Bartholomew's test in unbalanced one-way models," Computational Statistics & Data Analysis, Elsevier, vol. 34(1), pages 17-32, July.
    4. Boulesteix, Anne-Laure & Strobl, Carolin, 2007. "Maximally selected Chi-squared statistics and non-monotonic associations: An exact approach based on two cutpoints," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6295-6306, August.
    5. Hothorn, Torsten & Lausen, Berthold, 2003. "On the exact distribution of maximally selected rank statistics," Computational Statistics & Data Analysis, Elsevier, vol. 43(2), pages 121-137, June.
    6. Hothorn, Torsten & Hornik, Kurt & van de Wiel, Mark A. & Zeileis, Achim, 2006. "A Lego System for Conditional Inference," The American Statistician, American Statistical Association, vol. 60, pages 257-263, August.
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    Cited by:

    1. Edgar Merkle & Jinyan Fan & Achim Zeileis, 2014. "Testing for Measurement Invariance with Respect to an Ordinal Variable," Psychometrika, Springer;The Psychometric Society, vol. 79(4), pages 569-584, October.
    2. Carolin Strobl & Julia Kopf & Achim Zeileis, 2015. "Rasch Trees: A New Method for Detecting Differential Item Functioning in the Rasch Model," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 289-316, June.
    3. Xiaogang Su & George Ekow Quaye & Yishu Wei & Joseph Kang & Lei Liu & Qiong Yang & Juanjuan Fan & Richard A. Levine, 2024. "Smooth Sigmoid Surrogate (SSS): An Alternative to Greedy Search in Decision Trees," Mathematics, MDPI, vol. 12(20), pages 1-28, October.
    4. K. B. S. Huth & L. J. Waldorp & J. Luigjes & A. E. Goudriaan & R. J. Holst & M. Marsman, 2022. "A Note on the Structural Change Test in Highly Parameterized Psychometric Models," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 1064-1080, September.
    5. Qiu, Zhiping & Peng, Limin & Manatunga, Amita & Guo, Ying, 2019. "A smooth nonparametric approach to determining cut-points of a continuous scale," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 186-210.
    6. Rui Tang & Xiaoye Ma & Hui Yang & Michael Wolf, 2018. "Biomarker-Defined Subgroup Selection Adaptive Design for Phase III Confirmatory Trial with Time-to-Event Data: Comparing Group Sequential and Various Adaptive Enrichment Designs," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(2), pages 371-404, August.
    7. Dexin Chen & Meiting Fu & Liangjie Chi & Liyan Lin & Jiaxin Cheng & Weisong Xue & Chenyan Long & Wei Jiang & Xiaoyu Dong & Jian Sui & Dajia Lin & Jianping Lu & Shuangmu Zhuo & Side Liu & Guoxin Li & G, 2022. "Prognostic and predictive value of a pathomics signature in gastric cancer," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    8. Achim Zeileis & Torsten Hothorn, 2013. "A toolbox of permutation tests for structural change," Statistical Papers, Springer, vol. 54(4), pages 931-954, November.

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