Eqs. (61) and (62) provide maximum likelihood estimators only when the noise in which the signal is buried is Gaussian. There are general theorems in statistics indicating that the Gaussian noise is ubiquitous. One is the central limit theorem, which states that the mean of any set of variables with any distribution having a finite mean and variance tends to the normal distribution. The other comes from the information theory and says that the probability distribution of a random variable with a given mean and variance, which has the maximum entropy (minimum information) is the Gaussian distribution. Nevertheless, analysis of the data from gravitational-wave detectors shows that the noise in the detector may be non-Gaussian (see, e.g., Figure 6 in ). The noise in the detector may also be a non-linear and a non-stationary random process.
The maximum likelihood method does not require that the noise in the detector be Gaussian or stationary. However, in order to derive the optimum statistic and calculate the Fisher matrix we need to know the statistical properties of the data. The probability distribution of the data may be complicated, and the derivation of the optimum statistic, the calculation of the Fisher matrix components and the false alarm probabilities may be impractical. However, there is one important result that we have already mentioned. The matched-filter, which is optimal for the Gaussian case is also a linear filter that gives maximum signal-to-noise ratio no matter what the distribution of the data. Monte Carlo simulations performed by Finn  for the case of a network of detectors indicate that the performance of matched-filtering (i.e., the maximum likelihood method for Gaussian noise) is satisfactory for the case of non-Gaussian and stationary noise.
Allen et al. [10, 11] derived an optimal (in the Neyman–Pearson sense, for weak signals) signal processing strategy, when the detector noise is non-Gaussian and exhibits tail terms. This strategy is robust, meaning that it is close to optimal for Gaussian noise but far less sensitive than conventional methods to the excess large events that form the tail of the distribution. This strategy is based on a locally optimal test  that amounts to comparing a first non-zero derivative
The non-stationarity in the case of Gaussian and uncorrelated noise can be easily incorporated into matched filtering (see Appendix C of ). Let us assume that a noise sample in the data has a Gaussian pdf with a variance and zero mean (, where is the number of data points). Different noise samples may have distributions with different variances. We also assume that the noise samples are uncorrelated, then the autocorrelation function of the noise is given by [see Eq. (39)]
In the remaining part of this section we review some statistical tests and methods to detect non-Gaussianity, non-stationarity, and non-linearity in the data. A classical test for a sequence of data to be Gaussian is the Kolmogorov–Smirnov test . It calculates the maximum distance between the cumulative distribution of the data and that of a normal distribution, and assesses the significance of the distance. A similar test is the Lillifors test , but it adjusts for the fact that the parameters of the normal distribution are estimated from the data rather than specified in advance. Another test is the Jarque–Bera test , which determines whether sample skewness and kurtosis are unusually different from their Gaussian values.
A useful test to detect outliers in the data is Grubbs’ test . This test assumes that the data has an underlying Gaussian probability distribution but it is corrupted by some disturbances. Grubbs’ test detects outliers iteratively. Outliers are removed one by one and the test is iterated until no outliers are detected. Grubbs’ test is a test of the null hypothesis:
against the alternate hypothesis:
The Grubbs’ test statistic is the largest absolute deviation from the sample mean in units of the sample standard deviation, so it is defined as
Grubbs’ test has been used to identify outliers in the search of Virgo data for gravitational-wave signals from the Vela pulsar . A test to discriminate spurious events due to non-stationarity and non-Gaussianity of the data from genuine gravitational-wave signals has been developed by Allen . This test, called the time-frequency discriminator, is applicable to the case of broadband signals, such as those coming from compact coalescing binaries.
Let now and be two discrete-in-time random processes () and let be independent and identically distributed (i.i.d.) random variables. We call the process linear if it can be represented by
If Hypothesis 1 holds, we can test for linearity, that is, we have a second hypothesis testing problem:
If Hypothesis 4 holds, the process is linear.
Using the above tests we can detect non-Gaussianity and, if the process is non-Gaussian, non-linearity of the process. The distribution of the test statistic , Eq. (142), can be calculated in terms of distributions. For more details see .
It is not difficult to examine non-stationarity of the data. One can divide the data into short segments and for each segment calculate the mean, standard deviation and estimate the spectrum. One can then investigate the variation of these quantities from one segment of the data to the other. This simple analysis can be useful in identifying and eliminating bad data. Another quantity to examine is the autocorrelation function of the data. For a stationary process the autocorrelation function should decay to zero. A test to detect certain non-stationarities used for analysis of econometric time series is the Dickey–Fuller test . It models the data by an autoregressive process and it tests whether values of the parameters of the process deviate from those allowed by a stationary model. A robust test for detecting non-stationarity in data from gravitational-wave detectors has been developed by Mohanty . The test involves applying Student’s t-test to Fourier coefficients of segments of the data. Still another block-normal approach has been studied by McNabb et al. . It identifies places in the data stream where the characteristic statistics of the data change. These change points divide the data into blocks in characteristics are stationary.
Living Rev. Relativity 15, (2012), 4
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