Resampling Methods for Time Series

Brief communication "Improving the actual coverage of subsampling confidence intervals in atmospheric time series analysis"

Nonlinear Processes in Geophysics ◽

10.5194/npg-19-473-2012 ◽

2012 ◽

Vol 19 (5) ◽

pp. 473-477

Author(s):

A. Gluhovsky ◽

T. Nielsen

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Confidence Intervals ◽

Coverage Probability ◽

Resampling Methods ◽

Large Sample ◽

Series Analysis

Abstract. In atmospheric time series analysis, where only one record is typically available, subsampling (which works under the weakest assumptions among resampling methods), is especially useful. In particular, it yields large-sample confidence intervals of asymptotically correct coverage probability. Atmospheric records, however, are often not long enough, causing a substandard coverage of subsampling confidence intervals. In the paper, the subsampling methodology is extended to become more applicable in such practically important cases.

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Colored noise and computational inference in fMRI time series analysis: resampling methods in time and wavelet domains

NeuroImage ◽

10.1016/s1053-8119(01)91429-6 ◽

2001 ◽

Vol 13 (6) ◽

pp. 86 ◽

Cited By ~ 3

Author(s):

Ed Bullmore ◽

Chris Long ◽

John Suckling ◽

Jalal Fadili ◽

Gemma Calvert ◽

...

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Colored Noise ◽

Resampling Methods ◽

Series Analysis ◽

Computational Inference ◽

Fmri Time Series

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TESTING CHAOTIC DYNAMICS IN SYSTEMS WITH TWO POSITIVE LYAPUNOV EXPONENTS: A BOOTSTRAP SOLUTION

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127407017240 ◽

2007 ◽

Vol 17 (01) ◽

pp. 169-182 ◽

Cited By ~ 3

Author(s):

SIMONE GIANNERINI ◽

RODOLFO ROSA ◽

DIEGO LUIS GONZALEZ

Keyword(s):

Time Series ◽

Lyapunov Exponents ◽

Chaotic Dynamics ◽

Time Series Data ◽

Computer Experiments ◽

Series Data ◽

Resampling Methods ◽

Bufo Arenarum ◽

Toad Bufo

The present paper is devoted to the problem of detecting the presence of two positive Lyapunov exponents in time series data. In order to accomplish this task the accuracy of the estimates is essential, but existing estimation approaches do not provide it. We present a procedure exploiting resampling methods for building a statistical test for the presence of two positive exponents of comparable magnitudes through rigorous assessment of confidence intervals. The problem is studied by means of computer experiments performed in a variety of conditions on coupled Lorenz systems. Then, a case study regarding the time series of the cardiovascular activity of the toad Bufo Arenarum is presented. A comparison with other estimator algorithms is also shown.

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Time series analysis in the time domain and resampling methods for studies of functional magnetic resonance brain imaging

Human Brain Mapping ◽

10.1002/(sici)1097-0193(1997)5:3<168::aid-hbm3>3.0.co;2-1 ◽

1997 ◽

Vol 5 (3) ◽

pp. 168-193 ◽

Cited By ~ 74

Author(s):

Joseph J. Locascio ◽

Peggy J. Jennings ◽

Christopher I. Moore ◽

Suzanne Corkin

Keyword(s):

Time Series ◽

Magnetic Resonance ◽

Time Series Analysis ◽

Brain Imaging ◽

Time Domain ◽

Functional Magnetic Resonance ◽

Resampling Methods ◽

Series Analysis ◽

Magnetic Resonance Brain Imaging ◽

The Time Domain

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On the Analysis of Atmospheric and Climatic Time Series

Journal of Applied Meteorology and Climatology ◽

10.1175/jam2512.1 ◽

2007 ◽

Vol 46 (7) ◽

pp. 1125-1129 ◽

Cited By ~ 13

Author(s):

Alexander Gluhovsky ◽

Ernest Agee

Keyword(s):

Time Series ◽

Real Data ◽

Diagnostic Procedures ◽

Parametric Models ◽

Resampling Methods ◽

The Real ◽

Climatic Time Series ◽

Subsampling Method ◽

Climate Studies ◽

Climatic Series

Abstract Linear parametric models are commonly assumed and used for unknown data-generating mechanisms. This study demonstrates the value of inferring statistics of meteorological and climatological time series by using a computer-intensive subsampling method that allows one to avoid time series analysis anchored in parametric models with imposed perceived physical assumptions. A first-order autoregressive model, typically adopted as the default model for correlated time series in climate studies, has been selected and altered with a nonlinear component to provide insight into possible errors in estimation due to nonlinearities in the real data-generating mechanism. The nonlinearity undetected by basic diagnostic procedures is shown to invalidate statistical inference based on the linear model, whereas the inference derived through subsampling remains valid. It is argued that subsampling and other resampling methods are preferable in complex dependent-data situations that are typical for atmospheric and climatic series when the real data-generating mechanism is unknown.

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