
Review of Short Phrases and Links 
This Review contains major "Zero Mean" related terms, short phrases and links grouped together in the form of Encyclopedia article.
 The PHILLIPS option performs the PhillipsPerron test for three null hypothesis cases: zero mean, single mean, and deterministic trend.
 The residual, ê, is the difference between the actual Y and the predicted Y and has a zero mean.
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 Any tube of zero mean curvature has an invariant, the socalled flow vector.
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 This yields a oneparameter leptokurtic family with zero mean, unit variance, zero skewness, and arbitrary positive kurtosis.
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 In this section, the channel matrix and the noise vector are assumed to be complex Gaussian variables with zero mean and unit variance.
 The random term e ij is drawn from a normal distribution with a zero mean and an unknown variance s 2.
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 Note: Random error is typically assumed to be normally distributed with zero mean and a constant variance.
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 The are assumed to be independent, normal random variables with zero mean and constant variance.
 Residuals should be random, with zero mean and constant variance.
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 This frequency data is also normalized to zero mean by subtracting the average value, and then integrated to obtain phase values.
 Thus z scores have a zero mean and a standard deviation of 1: the classic normal distribution.
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 The matrix consist of random disturbances whose rows are uncorrelated multinormal with zero mean and a common covariance matrix.
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 Analogous to "every minimal surface has zero mean curvature" is the fact that every local minimum of a function of two variables is a critical point.
 The following pseudocode computes the first principal component of a data matrix, X T, with zero mean, without ever computing its covariance[ 10].
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 In the orthogonal matrix, white noise with zero mean and unit standard deviation added to the signal.
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 Here, (x, σ) is the Gaussian distribution with zero mean and variance σ 2.
 For an open soap film, the pressure difference is zero, hence the mean curvature is zero, and minimal surfaces have the property of zero mean curvature.
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 The random variable associated with this distribution comes about as the ratio of two Gaussian (normal) distributed variables with zero mean.
 Thus, with the above transformation, we can whiten the random vector to have zero mean and the identity covariance matrix.
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Zero Mean
 Let us consider, for instance, two random variables X1, X2, gaussian, with zero mean, variance 1, covariance r.
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 The variance of a random variable is its second moment only if the random variable has zero mean.
 The technique is invariant to inplane rotation (tilt) and robust to zero mean white noise.
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Categories
 Random Vector
 Gaussian
 Information > Evaluation > Analysis > Variance
 Random Error
 Senses > Hearing > Sound > Noise

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