Automatic detection and identification of shocks in Gaussian state-space models: A Bayesian approach

被引：1
|
作者：
Salvador, M [1 ]
Gargallo, P [1 ]
机构：
[1] Univ Zaragoza, Dept Stat Methods, Fac Econ & Business Studies, Zaragoza 50005, Spain
来源：
APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY | 2006年 / 22卷 / 01期
关键词：
Bayes factor; monitoring and intervention; model comparison; model selection; linear state-space models; Kalman filter;
D O I：
10.1002/asmb.608
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
An automatic monitoring and intervention algorithm that permits the supervision of very general aspects in an univariate linear Gaussian state-space model is proposed. The algorithm makes use of a model comparison and selection approach within a Bayesian framework. In addition, this algorithm incorporates the possibility of eliminating earlier interventions when subsequent evidence against them comes to light. Finally, the procedure is illustrated with two empirical examples taken from the literature. Copyright (C) 2005 John Wiley & Sons, Ltd.
引用
收藏
页码：17 / 39
页数：23
相关论文
共 50 条
  • [1] A Bayesian State-Space Approach for Damage Detection and Classification
    Dzunic, Zoran
    Chen, Justin G.
    Mobahi, Hossein
    Buyukozturk, Oral
    Fisher, John W., III
    DYNAMICS OF CIVIL STRUCTURES, VOL 2, 2015, : 171 - 183
  • [2] A Bayesian state-space approach for damage detection and classification
    Dzunic, Zoran
    Chen, Justin G.
    Mobahi, Hossein
    Buyukozturk, Oral
    Fisher, John W., III
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2017, 96 : 239 - 259
  • [3] Bayesian factorial linear Gaussian state-space models for biosignal decomposition
    Chiappa, Silvia
    Barber, David
    IEEE SIGNAL PROCESSING LETTERS, 2007, 14 (04) : 267 - 270
  • [4] Bayesian identification of state-space models via adaptive thermostats
    Umenberger, Jack
    Schon, Thomas B.
    Lindsten, Fredrik
    2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC), 2019, : 7382 - 7388
  • [5] A Sparse Bayesian Approach to the Identification of Nonlinear State-Space Systems
    Pan, Wei
    Yuan, Ye
    Goncalves, Jorge
    Stan, Guy-Bart
    IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2016, 61 (01) : 182 - 187
  • [6] A simple Bayesian state-space approach to the collective risk models
    Ahn, Jae Youn
    Jeong, Himchan
    Lu, Yang
    SCANDINAVIAN ACTUARIAL JOURNAL, 2023, 2023 (05) : 509 - 529
  • [7] A Bayesian Approach to Switching Linear Gaussian State-Space Models for Unsupervised Time-Series Segmentation
    Chiappa, Silvia
    SEVENTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, PROCEEDINGS, 2008, : 3 - 9
  • [8] Robust identification approach for nonlinear state-space models
    Liu, Xin
    Yang, Xianqiang
    NEUROCOMPUTING, 2019, 333 : 329 - 338
  • [9] Structured Variational Bayesian Inference for Gaussian State-Space Models With Regime Switching
    Petetin, Yohan
    Janati, Yazid
    Desbouvries, Francois
    IEEE SIGNAL PROCESSING LETTERS, 2021, 28 : 1953 - 1957
  • [10] BAYESIAN MODEL DISCRIMINATION AND BAYES FACTORS FOR LINEAR GAUSSIAN STATE-SPACE MODELS
    FRUHWIRTHSCHNATTER, S
    JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL, 1995, 57 (01): : 237 - 246
12345