Estimation of heteroscedastic measurement noise variances

被引:4
|
作者
de Brauwere, Anouk
Pintelon, Rik
De Ridder, Fjo
Schoukens, Johan
Baeyens, Willy
机构
[1] Vrije Univ Brussels, Dept Analyt & Environm Chem, B-1050 Brussels, Belgium
[2] Vrije Univ Brussels, Dept Elect & Instrumentat, B-1050 Brussels, Belgium
关键词
heteroscedasticity; noise variance estimation; residuals;
D O I
10.1016/j.chemolab.2006.09.001
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
For any quantitative data interpretation it is crucial to have information about the noise variances. Unfortunately, this information is often unavailable a priori. We propose a procedure to estimate the noise variances starting from the residuals. The method takes two difficulties into account. (i) The noise can be heteroscedastic (not constant over the measurement domain). This implies that one number is not enough anymore to characterise the total noise variance structure. (ii) The initial model used to generate the residuals may be imperfect. As a consequence, the residuals contain more than only stochastic information. The outcome of our procedure is an estimate of the noise variances which depends on the sample number, but is independent of the postulated model. A by-product of the procedure is information about the distribution of the degrees of freedom over the measurement domain. Indeed, as a consequence of the heteroscedastic noise, the model parameters will be fitted more to those data with low uncertainty and most of the degrees of freedom are lost at these locations. (c) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码:130 / 138
页数:9
相关论文
共 50 条
  • [41] Slope estimation in structural line-segment heteroscedastic measurement error models
    McAssey, Michael P.
    Hsieh, Fushing
    STATISTICS IN MEDICINE, 2010, 29 (25) : 2631 - 2642
  • [42] Intelligent estimation of noise and blur variances using ANN for the restoration of ultrasound images
    Uddin, Muhammad Shahin
    Halder, Kalyan Kumar
    Tahtali, Murat
    Lambert, Andrew J.
    Pickering, Mark R.
    Marchese, Margaret
    Stuart, Iain
    APPLIED OPTICS, 2016, 55 (31) : 8905 - 8915
  • [43] Robust Kalman estimation for system with uncertainties of noise variances and multiple networked inducements
    Yang C.-S.
    Jing B.-Q.
    Liu Z.
    Wang J.-Q.
    Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2021, 38 (10): : 1607 - 1618
  • [44] On Estimation of Time-Varying Variances of Source and Noise for Sensor Array Processing
    Pan, Chao
    Chen, Jingdong
    Shi, Guangming
    IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2020, 28 : 2865 - 2879
  • [45] How to keep the Type I Error Rate in ANOVA if Variances are Heteroscedastic
    Moder, Karl
    AUSTRIAN JOURNAL OF STATISTICS, 2007, 36 (03) : 179 - 188
  • [46] Density estimation with heteroscedastic error
    Delaigle, Aurore
    Meister, Alexander
    BERNOULLI, 2008, 14 (02) : 562 - 579
  • [47] ESTIMATION IN A HETEROSCEDASTIC REGRESSION MODEL
    RUTEMILLER, HC
    BOWERS, DA
    JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1968, 63 (322) : 552 - 557
  • [48] Robust Weighted Measurement Fusion Kalman Filter with Uncertain Parameters and Noise Variances
    Yang, Chunshan
    Deng, Zili
    PROCEEDINGS OF THE 2015 CHINESE INTELLIGENT AUTOMATION CONFERENCE: INTELLIGENT INFORMATION PROCESSING, 2015, 336 : 33 - 41
  • [49] Estimation of Heteroscedastic Multilinear Systems
    Wang, Mingliang
    Jacobsen, Elling W.
    Chotteau, Veronique
    Hjalmarsson, Hakan
    2020 59TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2020, : 2875 - 2880
  • [50] ESTIMATION WITH HETEROSCEDASTIC ERROR TERMS
    PARK, RE
    ECONOMETRICA, 1966, 34 (04) : 888 - &