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Construction of alternative hypotheses for randomization tests with ordinal outcomes

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  • Lu, Jiannan
  • Ding, Peng
  • Dasgupta, Tirthankar

Abstract

We construct sequences of alternative hypotheses in increasing order of departures from the sharp null hypothesis of zero treatment effect on each experimental unit, to help assess the powers of randomization tests with ordinal outcomes in randomized treatment-control experiments.

Suggested Citation

  • Lu, Jiannan & Ding, Peng & Dasgupta, Tirthankar, 2015. "Construction of alternative hypotheses for randomization tests with ordinal outcomes," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 348-355.
    • Handle: RePEc:eee:stapro:v:107:y:2015:i:c:p:348-355
    DOI: 10.1016/j.spl.2015.09.013
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    References listed on IDEAS

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    1. Rosenbaum, Paul R., 2010. "Design Sensitivity and Efficiency in Observational Studies," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 692-702.
    2. Chuan Ju & Zhi Geng, 2010. "Criteria for surrogate end points based on causal distributions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 129-142, January.
    3. R. Bradley & S. Katti & Irma Coons, 1962. "Optimal scaling for ordered categories," Psychometrika, Springer;The Psychometric Society, vol. 27(4), pages 355-374, December.
    4. Jing Cheng, 2009. "Estimation and Inference for the Causal Effect of Receiving Treatment on a Multinomial Outcome," Biometrics, The International Biometric Society, vol. 65(1), pages 96-103, March.
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    Cited by:

    1. Lu, Jiannan, 2018. "On the partial identification of a new causal measure for ordinal outcomes," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 1-7.
    2. Lu, Jiannan, 2016. "On randomization-based and regression-based inferences for 2K factorial designs," Statistics & Probability Letters, Elsevier, vol. 112(C), pages 72-78.
    3. Lu, Jiannan, 2016. "Covariate adjustment in randomization-based causal inference for 2K factorial designs," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 11-20.

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