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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Information Generating Function of Ranked Set Samples.

Omid Kharazmi¹, Mostafa Tamandi¹, Narayanaswamy Balakrishnan²

¹Department of Statistics, Faculty of Mathematical Sciences, Vali-e-Asr University of Rafsanjan, Rafsanjan P.O. Box 518, Iran.

Entropy (Basel, Switzerland)

|November 27, 2021

Summary

This summary is machine-generated.

This study introduces information generating (IG) and relative information generating (RIG) measures for ranked set sampling (RSS) with unequal sample sizes. Comparisons with simple random sampling (SRS) highlight RSS

Keywords:

Kullback–Leibler divergence information generating function ranked set sampling relative information generating function simple random sampling

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Area of Science:

Statistics
Probability Theory
Statistical Inference

Background:

Ranked set sampling (RSS) is an efficient sampling technique.
Information generating (IG) and relative information generating (RIG) functions are key measures in information theory and statistics.
Understanding sampling scheme efficiency is crucial for data analysis.

Purpose of the Study:

To investigate Information Generating (IG) and Relative Information Generating (RIG) functions for maximum and minimum ranked set sampling (RSS) with unequal sample sizes.
To compare the performance of RSS schemes against simple random sampling (SRS) using IG measures.
To analyze the RIG divergence between SRS and RSS frameworks.

Main Methods:

Calculation of IG and RIG functions for unequal size RSS schemes.
Examination of IG measures for simple random sampling (SRS).
Application of dispersive stochastic ordering for SRS vs. RSS comparison.
Discussion of RIG divergence measures.

Main Results:

The study provides novel IG and RIG measures for unequal size RSS.
Comparisons reveal differences in information generation between SRS and RSS.
Dispersive stochastic ordering offers insights into the efficiency of sampling methods.
RIG divergence quantifies differences between SRS and RSS.

Conclusions:

Unequal size RSS schemes offer distinct information generation properties compared to SRS.
The developed IG and RIG measures provide valuable tools for evaluating sampling strategies.
RSS procedures demonstrate potential advantages over SRS in certain statistical contexts.