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

被引：0

作者：

Ghoreishi, S. K. ^{[1
]}

Wu, Jingjing ^{[2
]}

Ghoreishi, Ghazal S. ^{[3
]}

机构：

[1] Univ Qom, Dept Stat, Qom, Iran

[2] Univ Calgary, Dept Math & Stat, Calgary, AB, Canada

[3] Shahid Beheshti Univ, Fac Elect Engn, Tehran, Iran

来源：

JOURNAL OF THE KOREAN STATISTICAL SOCIETY | 2023年 / 52卷 / 01期

关键词：

Empirical likelihood; Gene expression; Penalty function; Threshold functions; MICROARRAY EXPERIMENTS; EMPIRICAL BAYES; ANOVA;

D O I：

10.1007/s42952-022-00198-w

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：234 / 247

页数：14

共 18 条

[1] Bayesian analysis of restricted penalized empirical likelihood [J].