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More efficient logistic analysis using moving extreme ranked set sampling

Author

Listed:
  • Hani M. Samawi
  • Haresh Rochani
  • Daniel Linder
  • Arpita Chatterjee

Abstract

Logistic regression is the most popular technique available for modeling dichotomous-dependent variables. It has intensive application in the field of social, medical, behavioral and public health sciences. In this paper we propose a more efficient logistic regression analysis based on moving extreme ranked set sampling (MERSSmin) scheme with ranking based on an easy-to-available auxiliary variable known to be associated with the variable of interest (response variable). The paper demonstrates that this approach will provide more powerful testing procedure as well as more efficient odds ratio and parameter estimation than using simple random sample (SRS). Theoretical derivation and simulation studies will be provided. Real data from 2011 Youth Risk Behavior Surveillance System (YRBSS) data are used to illustrate the procedures developed in this paper.

Suggested Citation

  • Hani M. Samawi & Haresh Rochani & Daniel Linder & Arpita Chatterjee, 2017. "More efficient logistic analysis using moving extreme ranked set sampling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 753-766, March.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:4:p:753-766
    DOI: 10.1080/02664763.2016.1182136
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    References listed on IDEAS

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    1. King, Gary & Zeng, Langche, 2001. "Logistic Regression in Rare Events Data," Political Analysis, Cambridge University Press, vol. 9(2), pages 137-163, January.
    2. Al-Saleh, M. Fraiwan & Al-Kadiri, M. Ali, 2000. "Double-ranked set sampling," Statistics & Probability Letters, Elsevier, vol. 48(2), pages 205-212, June.
    3. Maalouf, Maher & Trafalis, Theodore B., 2011. "Robust weighted kernel logistic regression in imbalanced and rare events data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 168-183, January.
    4. Mohammad Al-Saleh & Said Al-Hadhrami, 2003. "Estimation of the mean of the exponential distribution using moving extremes ranked set sampling," Statistical Papers, Springer, vol. 44(3), pages 367-382, July.
    5. Gang Zheng & Mohammad Al-Saleh, 2003. "Improving the best linear unbiased estimator for the scale parameter of symmetric distributions by using the absolute value of ranked set samples," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(3), pages 253-265.
    6. Zehua Chen & You-Gan Wang, 2004. "Efficient Regression Analysis with Ranked-Set Sampling," Biometrics, The International Biometric Society, vol. 60(4), pages 997-1004, December.
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    Cited by:

    1. Elham Zamanzade & Majid Asadi & Afshin Parvardeh & Ehsan Zamanzade, 2023. "A ranked-based estimator of the mean past lifetime with an application," Statistical Papers, Springer, vol. 64(1), pages 161-177, February.
    2. Ehsan Zamanzade & M. Mahdizadeh & Hani M. Samawi, 2020. "Efficient estimation of cumulative distribution function using moving extreme ranked set sampling with application to reliability," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(3), pages 485-502, September.
    3. Santu Ghosh & Arpita Chatterjee & N. Balakrishnan, 2017. "Nonparametric confidence intervals for ranked set samples," Computational Statistics, Springer, vol. 32(4), pages 1689-1725, December.

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