文件名称:em_covariances
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Using SAS/IML :
This code uses the EM algorithm to estimate the maximum likelihood (ML) covariance matrix and mean vector in the presence of missing data. This implementation of the EM algorithm or any similar ML approach assumes that the data are missing completely at random (MCAR) or missing at random (MAR: see Little & Rubin, 1987).-Using SAS/IML :
This code uses the EM algorithm to estimate the maximum likelihood (ML) covariance matrix and mean vector in the presence of missing data. This implementation of the EM algorithm or any similar ML approach assumes that the data are missing completely at random (MCAR) or missing at random (MAR: see Little & Rubin, 1987).
This code uses the EM algorithm to estimate the maximum likelihood (ML) covariance matrix and mean vector in the presence of missing data. This implementation of the EM algorithm or any similar ML approach assumes that the data are missing completely at random (MCAR) or missing at random (MAR: see Little & Rubin, 1987).-Using SAS/IML :
This code uses the EM algorithm to estimate the maximum likelihood (ML) covariance matrix and mean vector in the presence of missing data. This implementation of the EM algorithm or any similar ML approach assumes that the data are missing completely at random (MCAR) or missing at random (MAR: see Little & Rubin, 1987).
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em_covariances.sas