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Non-parametric comparison and classification of two large-scale populations

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Abstract

In this paper, we investigate a non-parametric approach to compare two groups in microarray data. This is done using a threshold penalized-distance likelihood function, which is made up of a penalty and a suitable threshold distance, and is applicable when sample size is small or when the data is not normally distributed. We also use this function to classify new data. This is based on objects that are identified as differences between the two groups, not for all objects. We also study a real data application to illustrate our methods.

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Data availability

We used Efron’s microarray prostate data (singh2002 data set available in “sda” library in R software).

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Authors and Affiliations

  1. Department of Statistics, University of Qom, Qom, Iran

    S. K. Ghoreishi

  2. Department of Mathematics and Statistics, University of Calgary, Calgary, Canada

    Jingjing Wu

  3. Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran

    Ghazal S. Ghoreishi

Authors
  1. S. K. Ghoreishi
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  2. Jingjing Wu
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  3. Ghazal S. Ghoreishi
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Correspondence to S. K. Ghoreishi.

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In this work, we have used a well-known dataset to apply our methodology, so we have no ethical conduct.

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Ghoreishi, S.K., Wu, J. & Ghoreishi, G.S. Non-parametric comparison and classification of two large-scale populations. J. Korean Stat. Soc. 52, 234–247 (2023). https://doi.org/10.1007/s42952-022-00198-w

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  • DOI: https://doi.org/10.1007/s42952-022-00198-w

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