The Statistical Analysis of Time Series (T. W. Anderson)

SIAM Review ◽  
1976 ◽  
Vol 18 (2) ◽  
pp. 313-314
Author(s):  
Andrey Feuerverger
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Analysis Of Time Series

The Statistical Analysis of Time Series.

1973 ◽  
Vol 68 (341) ◽  
pp. 241
Author(s):  
Herbert T. Davis ◽  
T. W. Anderson
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Analysis Of Time Series

scholarly journals 1P-168 Statistical analysis of time series of psudopodia formation in Dictyostelium cells(Cell biology, The 47th Annual Meeting of the Biophysical Society of Japan)

2009 ◽  
Vol 49 (supplement) ◽  
pp. S89
Author(s):  
Tatsuo Shibata ◽  
Naohiro Akuzawa ◽  
Satoshi Fujise ◽  
Akihiro Nagamatsu ◽  
Masatoshi Nishikawa
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Annual Meeting ◽  
Cell Biology ◽  
Biophysical Society ◽  
Analysis Of Time Series

scholarly journals Scale-space analysis of time series in circulatory research

2006 ◽  
Vol 291 (6) ◽  
pp. H3012-H3022 ◽  
Author(s):  
Kim Erlend Mortensen ◽  
Fred Godtliebsen ◽  
Arthur Revhaug
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Real Time ◽  
Repeated Measures ◽  
Multiple Scales ◽  
Visual Presentation ◽  
Scale Space ◽  
Statistical Tool ◽  
Data Points ◽  
Analysis Of Time Series

Statistical analysis of time series is still inadequate within circulation research. With the advent of increasing computational power and real-time recordings from hemodynamic studies, one is increasingly dealing with vast amounts of data in time series. This paper aims to illustrate how statistical analysis using the significant nonstationarities (SiNoS) method may complement traditional repeated-measures ANOVA and linear mixed models. We applied these methods on a dataset of local hepatic and systemic circulatory changes induced by aortoportal shunting and graded liver resection. We found SiNoS analysis more comprehensive when compared with traditional statistical analysis in the following four ways: 1) the method allows better signal-to-noise detection; 2) including all data points from real time recordings in a statistical analysis permits better detection of significant features in the data; 3) analysis with multiple scales of resolution facilitates a more differentiated observation of the material; and 4) the method affords excellent visual presentation by combining group differences, time trends, and multiscale statistical analysis allowing the observer to quickly view and evaluate the material. It is our opinion that SiNoS analysis of time series is a very powerful statistical tool that may be used to complement conventional statistical methods.

CHAOS IN HETEROGENEOUS NETWORKS WITH TEMPORALLY INERT NODES

2009 ◽  
Vol 19 (02) ◽  
pp. 677-686
Author(s):  
J. J. TORRES ◽  
J. MARRO ◽  
S. DE FRANCISCIS
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Statistical Analysis ◽  
Power Law ◽  
Heterogeneous Networks ◽  
Heterogeneous Distribution ◽  
Dynamic Attractors ◽  
Short Time ◽  
Degree Of Order ◽  
Analysis Of Time Series

We discuss an attractor neural network in which only a fraction ρ of nodes is simultaneously updated. In addition, the network has a heterogeneous distribution of connection weights and, depending on the current degree of order, connections are changed at random by a factor Φ on short-time scales. The resulting dynamic attractors may become unstable in a certain range of Φ thus ensuing chaotic itineracy which highly depends on ρ. For intermediate values of ρ, we observe that the number of attractors visited increases with ρ, and that the trajectory may change from regular to chaotic and vice versa as ρ is modified. Statistical analysis of time series shows a power-law spectra under conditions in which the attractors' space is most efficiently explored.

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