Estimating the number of signals using principal component analysis

被引：5
|
作者：
Virta, Joni [1 ]
Nordhausen, Klaus [2 ]
机构：
[1] Aalto Univ, Dept Math & Syst Anal, Espoo, Finland
[2] Vienna Univ Technol, Inst Stat & Math Methods Econ, Wiedner Hauptstr 7, A-1040 Vienna, Austria
来源：
STAT | 2019年 / 8卷 / 01期
关键词：
bootstrap; covariance matrix; dimension reduction; independent component analysis; order determination; EIGENVALUES; COVARIANCE; TESTS;
D O I：
10.1002/sta4.231
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 work, we develop inferential tools for determining the correct number of principal components under a general noisy latent variable model, which includes as a special case, for example, the noisy independent component model. The problem is approached using hypothesis testing, and we provide both a large-sample test and several resampling-based alternatives. Simulations and an application to sound data reveal that both types of approaches keep the desired levels and have good power.
引用
收藏
页数：7
相关论文
共 50 条
  • [1] ESTIMATING OF RUSSIAN UNIVERSITIES COMPETITIVENESS USING PRINCIPAL COMPONENT ANALYSIS
    Korf, Vasily
    [J]. BIZNES INFORMATIKA-BUSINESS INFORMATICS, 2014, 28 (02): : 63 - 71
  • [2] Calculation of area of stabilometric signals using principal component analysis
    Oliveira, LF
    Simpson, DM
    Nadal, J
    [J]. PHYSIOLOGICAL MEASUREMENT, 1996, 17 (04) : 305 - 312
  • [3] Interpolation of signals with missing data using Principal Component Analysis
    Oliveira, P.
    Gomes, L.
    [J]. MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING, 2010, 21 (01) : 25 - 43
  • [4] Interpolation of signals with missing data using Principal Component Analysis
    P. Oliveira
    L. Gomes
    [J]. Multidimensional Systems and Signal Processing, 2010, 21 : 25 - 43
  • [5] Estimating the number of sources using independent component analysis
    Sawada, Hiroshi
    Mukai, Ryo
    Araki, Shoko
    Makino, Shoji
    [J]. ACOUSTICAL SCIENCE AND TECHNOLOGY, 2005, 26 (05) : 450 - 452
  • [6] Identification of Excitation Source Number using Principal Component Analysis
    Dong, Jian Chao
    Yang, Tie Jun
    Li, Xin Hui
    Shuai, Zhi Jun
    Xiao, You Hong
    [J]. ADVANCES IN MECHANICAL DESIGN, PTS 1 AND 2, 2011, 199-200 : 850 - 857
  • [7] VECTOR QUANTIZATION OF SPEECH SIGNALS USING PRINCIPAL COMPONENT ANALYSIS.
    Kohata, Minoru
    Sone, Hideaki
    Echigo, Hiroshi
    Takagi, Tasaku
    [J]. Electronics and Communications in Japan, Part I: Communications (English translation of Denshi Tsushin Gakkai Ronbunshi), 1987, 70 (05): : 16 - 26
  • [8] Artifacts Removal of EEG Signals using Adaptive Principal Component Analysis
    Turnip, Arjon
    Kusumandari, Dwi Esti
    [J]. PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON COMPUTATION FOR SCIENCE AND TECHNOLOGY, 2015, 5 : 172 - 175
  • [9] Correction of Copy Number Variation Data Using Principal Component Analysis
    Chen, Jiayu
    Liu, Jingyu
    Calhoun, Vince D.
    [J]. 2010 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS (BIBMW), 2010, : 827 - 828
  • [10] Estimating the Dominant Orientation of an Object Using Image Segmentation and Principal Component Analysis
    Bhagavatula, Sravan
    Sephus, Nashlie
    [J]. ADVANCES IN VISUAL COMPUTING, PT I (ISVC 2015), 2015, 9474 : 243 - 252
12345