| 31. | The covariance matrix adaptation ( CMA ) is a method to update the covariance matrix of this distribution.
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| 32. | The update equations for mean and covariance matrix maximize a likelihood while resembling an expectation-maximization algorithm.
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| 33. | In a mean-variance optimization framework, accurate estimation of the variance-covariance matrix is paramount.
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| 34. | In a practical application in portfolio optimization, accurate estimation of the variance-covariance matrix is paramount.
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| 35. | Similarly, random vectors whose covariance matrix is zero in every entry outside the main diagonal are called uncorrelated.
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| 36. | In an MPT or mean-variance optimization framework, accurate estimation of the variance covariance matrix is paramount.
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| 37. | What if the covariance matrix is not known a-priori and needs to be estimated from the data?
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| 38. | For example, in the case of a Gaussian distribution, this comprises the mean and the covariance matrix.
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| 39. | If X is a positive semi-definite square matrix, commonly referred to as the variance-covariance matrix.
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| 40. | Assuming the missing data are missing at random this results in an estimate for the covariance matrix which is unbiased.
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