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Probabilistic index models

Author

Listed:
  • Olivier Thas
  • Jan De Neve
  • Lieven Clement
  • Jean-Pierre Ottoy

Abstract

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Suggested Citation

  • Olivier Thas & Jan De Neve & Lieven Clement & Jean-Pierre Ottoy, 2012. "Probabilistic index models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(4), pages 623-671, September.
    • Handle: RePEc:bla:jorssb:v:74:y:2012:i:4:p:623-671
    DOI: j.1467-9868.2011.01020.x
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    Citations

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    Cited by:

    1. Lu Mao & Tuo Wang, 2021. "A class of proportional win‐fractions regression models for composite outcomes," Biometrics, The International Biometric Society, vol. 77(4), pages 1265-1275, December.
    2. Amorim, G. & Thas, O. & Vermeulen, K. & Vansteelandt, S. & De Neve, J., 2018. "Small sample inference for probabilistic index models," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 137-148.
    3. Jentsch, Carsten & Leucht, Anne, 2014. "Bootstrapping Sample Quantiles of Discrete Data," Working Papers 14-15, University of Mannheim, Department of Economics.
    4. Dennis Dobler & Markus Pauly, 2018. "Bootstrap- and permutation-based inference for the Mann–Whitney effect for right-censored and tied data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 639-658, September.
    5. Daniel Fischer & Hannu Oja & Johanna Schleutker & Pranab K. Sen & Tiina Wahlfors, 2014. "Generalized Mann–Whitney Type Tests for Microarray Experiments," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(3), pages 672-692, September.
    6. Fan, Chunpeng & Zhang, Donghui, 2014. "Wald-type rank tests: A GEE approach," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 1-16.
    7. Jan De Neve & Olivier Thas, 2015. "A Regression Framework for Rank Tests Based on the Probabilistic Index Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1276-1283, September.
    8. Alan Agresti & Claudia Tarantola, 2018. "Simple ways to interpret effects in modeling ordinal categorical data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(3), pages 210-223, August.
    9. Ejike R. Ugba & Daniel Mörlein & Jan Gertheiss, 2021. "Smoothing in Ordinal Regression: An Application to Sensory Data," Stats, MDPI, vol. 4(3), pages 1-18, July.
    10. Debajit Chatterjee & Uttam Bandyopadhyay, 2019. "Testing in nonparametric ANCOVA model based on ridit reliability functional," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(2), pages 327-364, April.
    11. Alan Agresti & Maria Kateri, 2017. "Ordinal probability effect measures for group comparisons in multinomial cumulative link models," Biometrics, The International Biometric Society, vol. 73(1), pages 214-219, March.
    12. Edgar Brunner & Frank Konietschke & Arne C. Bathke & Markus Pauly, 2021. "Ranks and Pseudo‐ranks—Surprising Results of Certain Rank Tests in Unbalanced Designs," International Statistical Review, International Statistical Institute, vol. 89(2), pages 349-366, August.
    13. Dennis Dobler & Sarah Friedrich & Markus Pauly, 2020. "Nonparametric MANOVA in meaningful effects," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(4), pages 997-1022, August.

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