Comparing non-stationary and irregularly spaced time series
In this paper, we present approximate distributions for the ratio of the cumulative wavelet periodograms considering stationary and non-stationary time series generated from independent Gaussian processes. We also adapt an existing procedure to use this statistic and its approximate distribution in...
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| Veröffentlicht in: | Computational statistics & data analysis 2012-12, Vol.56 (12), p.3921-3934 |
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| container_title | Computational statistics & data analysis |
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| creator | Salcedo, Gladys E. Porto, Rogério F. Morettin, Pedro A. |
| description | In this paper, we present approximate distributions for the ratio of the cumulative wavelet periodograms considering stationary and non-stationary time series generated from independent Gaussian processes. We also adapt an existing procedure to use this statistic and its approximate distribution in order to test if two regularly or irregularly spaced time series are realizations of the same generating process. Simulation studies show good size and power properties for the test statistic. An application with financial microdata illustrates the test usefulness. We conclude advocating the use of these approximate distributions instead of the ones obtained through randomizations, mainly in the case of irregular time series. |
| doi_str_mv | 10.1016/j.csda.2012.05.022 |
| format | Article |
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| source | Elsevier ScienceDirect Journals Complete - AutoHoldings |
| subjects | Approximation Computer simulation Data processing Distributions of quadratic forms Gaussian Hypothesis testing Irregularly spaced time series Locally stationary wavelet processes Multiresolution approximation Randomization Statistics Time series Wavelet |
| title | Comparing non-stationary and irregularly spaced time series |
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