Advertisement

Feedback Analysis of a Living Body by a Multivariate Autoregressive Model

  • Takao Wada
Chapter
  • 576 Downloads
Part of the Statistics for Engineering and Physical Science book series (ISS)

Abstract

In the fields of medical or biological science, time series analysis including the analysis of fluctuation become popular in recent years. In spite of the fact, the analysis of the univariate system is mostly used and the analysis of the multivariate system is very few. Of course, there are some handbooks for multivariate time series analysis for medical data. However even in these handbooks, only the coherency among mutual variables, i.e. correlation, although it is classified according to the frequency, is referred to as a pivotal factor and lacks the consideration on the effect of feedback. It should be recognized that such a situation is quite regrettable considering from medical researchers’ viewpoint. Perhaps it is due to the fact that, in addition to the difficulty in taking in the multivariate data for time series analysis, the difficulty in the analysis or absence of methods usable for analysis of actual feedback systems.

Keywords

Impulse Response Proximal Tubule Feedback System Impulse Response Function Impulsive Noise 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Akaike, H. and Nakagawa, T. (1988), Statistical Analysis and Control of Dynamic Systems, Kluwer Academic Publishers, Dordrecht. (Original Japanese version was published in 1972 from Saiensu-sha.)zbMATHGoogle Scholar
  2. Holstein-Rathlow, N.H. and Marsh, D.J. (1989), “Oscillations of tubular pressure, flow, and distal chloride concentration in rats,” Amer. J. Physiol., Vol. 256, F1007-F1014.Google Scholar
  3. Wada, T., (1989), “Analysis of clinical data by multivariate autoregressive model,” Medical Informatics, Vol. 9, 263–272 (in Japanese).Google Scholar
  4. Wada, T., (1990), “Analysis of clinical data by multivariate autoregressive model (Akaike model),” Morbid Pathology, Vol. 9, 984–990 (Japanese).Google Scholar
  5. Wada, T. (1994), “Multivariate autoregressive modeling for analysis of biomedical systems with feedback,” Proceedings of the First US/Japan Conference on Frontiers of Statistical Modeling, Kluwer Academic Publishers, 293–317.Google Scholar
  6. Wada, T., Akaike, H., Yamada, H. et al. (1988), “Application of multivariate autoregressive modeling for analysis of immunologic networks in man,” Comput. Math. Appl., Vol. 15, 713–722.zbMATHCrossRefGoogle Scholar
  7. Wada, T., Jinnouchi, M. and Matsumura, Y. (1988), “Application of autoregressive modeling for the analysis of clinical and other biological data,” Ann. Inst. Statist. Math., Vol. 40, 211–227.CrossRefGoogle Scholar
  8. Wada, T., Kojima, F., Aoyagi, T. and Umezawa, H. (1988), “Feedback analysis of renin-angiotensin system under the effect of angiotensin converting enzyme inhibitors,” Biotech. Appl. Biochem., Vol. 10, 435–446.Google Scholar
  9. Wada, T., Yamada, H., Inoue, H., Iso, T., Udagawa, E. and Kuroda, S. (1990), “Clinical usefulness of multivariate autoregressive modeling as a tool for analyzing T-lymphocyte subset fluctuations,” Math. Comput. Model, Vol. 14, 610–613.zbMATHCrossRefGoogle Scholar
  10. Wada, T., Sato, S. and Matuo, N. (1993), “Application of multivariate autoregressive modeling for analyzing chloride-potassium-bicarbonate relationship in the body,” Med. Biol. Eng. Comput., Vol. 31, 99–107.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag New York, Inc. 1999

Authors and Affiliations

  • Takao Wada

There are no affiliations available

Personalised recommendations