Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-27T03:19:41.699Z Has data issue: false hasContentIssue false
  • English
  • Français

Spectral Analysis of Twin Time Series Designs

Published online by Cambridge University Press:  01 August 2014

P.C.M. Molenaar  and
D.I. Boomsma
P.C.M. Molenaar*
Affiliation:
Department of Psychology, University of Amsterdam, Netherlands
D.I. Boomsma
Affiliation:
Department of Experimental Psychology, Free University, Amsterdam, Netherlands
*
Department of Psychology, University of Amsterdam, Weesperplein 8, 1018 XA Amsterdam, Netherlands

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The genetic analysis of physiological time series has to accommodate the presence of autocorrelation. This can be accomplished by means of orthogonal transformation of the series, thus enabling the use of standard genetic analysis techniques for the sequence of uncorrelated transforms. In view of the oscillatory character which typifies various physiological time series, it is customary to invoke spectral techniques for the analysis of these series. It can be shown that spectral analysis is an orthogonal transformation that asymptotically resembles principal component analysis. Consequently, standard genetic analysis methods for the uncorrelated spectral transforms may be used. This approach will be illustrated with simulated and real (heart rate) data for univariate twin time series. Furthermore, it will be indicated that the proposed analysis can be readily generalized to multivariate time series.

Keywords

Spectral analysisTwin dataHeart rateTime series
Type
Research Article
Information
Acta geneticae medicae et gemellologiae: twin research , Volume 36 , Issue 1 , January 1987 , pp. 51 - 59
Copyright
Copyright © The International Society for Twin Studies 1987

References

REFERENCES

1.Brillinger, DR (1973): The analysis of time series collected in an experimental design. In Krishnaiah, PR (ed): Multivariate analysis III. New York: Academic Press, pp 241–256.Google Scholar
2.DeBoer, RW, Karemaker, JM (1985): The phase between respiration and heart-rate variability. Proceedings 38th Annual Conference on Engineering in Medicine and Biology.Google Scholar
3.Molenaar, PCM, Boomsma, DI (1987): The genetic analysis of repeated measures II: The Kar-hunen-Loeve expansion. Behav Genet (in press).Google Scholar
4.Molenaar, PCM, Molen, MW van der (1985): Global models: A viable compromis between content specificity and ease of application to heart rate changes. In Orlebeke, JF, Mulder, G, Doornen, LJP (eds): Psychophysiology of cardiovascular control. Models, methods and data. New York: Plenum press, pp 375–390.Google Scholar