A test statistic with a ranking method based on the Jeffreys divergence measure

被引:0
|
作者
Murakami, Hidetoshi [1 ]
Kawada, Soshi [2 ]
机构
[1] Tokyo Univ Sci, Dept Appl Math, Tokyo, Japan
[2] Tokyo Univ Sci, Grad Sch Sci, Dept Appl Math, Tokyo, Japan
关键词
Jeffreys divergence measure; Kullback-Liebler divergence measure; power comparison; ranking method; 2-SAMPLE;
D O I
10.1080/03610918.2019.1649424
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
Nonparametric two-sample tests are important statistical procedures in many scientific fields. The test statistic has been derived from the Kullback-Liebler divergence between two empirical distribution functions. This study modifies a nonparametric two-sample test statistic using a ranking method. The modified test statistic is shown to be based on the Jeffreys divergence measure. The exact critical value of the proposed test statistic is derived for small sample sizes. Simulations are used to investigate the power of the proposed test statistic for the location alternative and for any difference between distributions, with various population distributions, for small sample sizes.
引用
收藏
页码:266 / 279
页数:14
相关论文
共 50 条
  • [1] Fuzzy c-means clustering using Jeffreys-divergence based similarity measure
    Seal, Ayan
    Karlekar, Aditya
    Krejcar, Ondrej
    Gonzalo-Martin, Consuelo
    [J]. APPLIED SOFT COMPUTING, 2020, 88
  • [2] Statistic Based Iterative Method to Measure Inner Radius of Tube
    Kim, Hyoung-Seok
    Naranbaatar, Erdenesuren
    Lee, Byung-Ryong
    [J]. INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2010), 2010, : 1538 - 1543
  • [3] Clustering Uncertain Data Objects Using Jeffreys-Divergence and Maximum Bipartite Matching Based Similarity Measure
    Sharma, Krishna Kumar
    Seal, Ayan
    Yazidi, Anis
    Selamat, Ali
    Krejcar, Ondrej
    [J]. IEEE Access, 2021, 9 : 79505 - 79519
  • [4] Clustering Uncertain Data Objects Using Jeffreys-Divergence and Maximum Bipartite Matching Based Similarity Measure
    Sharma, Krishna Kumar
    Seal, Ayan
    Yazidi, Anis
    Selamat, Ali
    Krejcar, Ondrej
    [J]. IEEE ACCESS, 2021, 9 : 79505 - 79519
  • [5] Bahadur efficiency of the phi-divergence test statistic
    Pardo, L
    [J]. SOFT METHODOLOGY AND RANDOM INFORMATION SYSTEMS, 2004, : 307 - 314
  • [6] Test Analysis on the Model of Expression Based on the athematical Statistic Method
    Yu, Jianhong
    [J]. ADVANCES IN INTELLIGENT STRUCTURE AND VIBRATION CONTROL, 2012, 160 : 150 - 153
  • [7] Image classification based on Markov random field models with Jeffreys divergence
    Nishii, Ryuei
    Eguchi, Shinto
    [J]. JOURNAL OF MULTIVARIATE ANALYSIS, 2006, 97 (09) : 1997 - 2008
  • [8] Reproducibility-optimized test statistic for ranking genes in microarray studies
    Elo, Laura L.
    Filen, Sanna
    Lahesmaa, Riitta
    Aittokallio, Tero
    [J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2008, 5 (03) : 423 - 431
  • [9] Generalization of Jeffreys divergence-based priors for Bayesian hypothesis testing
    Bayarri, M. J.
    Garcia-Donato, G.
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 2008, 70 : 981 - 1003
  • [10] Active contour model for inhomogenous image segmentation based on Jeffreys divergence
    Han, Bin
    Wu, Yiquan
    [J]. PATTERN RECOGNITION, 2020, 107