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An R-Based Landscape Validation of a Competing Risk Model
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On partial randomized response model using ranked set sampling.

Azhar Mehmood Abbasi1,2, Muhammad Yousaf Shad2, Aneel Ahmed2

  • 1Department of IT and Computer Science Pak-Austria Fachhochschule, Institute of Applied Sciences & Technology, Haripur, Pakistan.

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Summary
This summary is machine-generated.

This study introduces a new partial randomized response technique for collecting sensitive data in ranked set sampling. The method enhances respondent cooperation and improves population proportion estimation, outperforming existing techniques.

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Area of Science:

  • Statistics
  • Survey Methodology
  • Biostatistics

Background:

  • Collecting sensitive data reliably is challenging.
  • Ranked set sampling (RSS) offers efficient data collection but faces hurdles with sensitive information.
  • Auxiliary information can improve estimation accuracy.

Purpose of the Study:

  • To propose a novel partial randomized response technique for sensitive data collection within an RSS framework.
  • To enhance respondent confidence and cooperation in surveys involving sensitive questions.
  • To improve the estimation of population proportions using auxiliary information.

Main Methods:

  • Developed a partial randomized response technique allowing respondents a choice between direct and randomized answers.
  • Integrated this technique into the ranked set sampling (RSS) scheme.
  • Analyzed the statistical properties and compared performance against existing randomized response methods.
  • Conducted a cost-effectiveness analysis.

Main Results:

  • The proposed partial randomized response technique demonstrated improved data reliability and respondent cooperation.
  • The method proved superior to existing randomized response techniques in estimation accuracy and efficiency.
  • Cost analysis confirmed the economic advantages of the suggested approach.

Conclusions:

  • The partial randomized response technique is a valuable advancement for collecting sensitive data in RSS.
  • This method offers a practical and cost-effective solution for surveys requiring sensitive information, such as in clinical trials.