{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T10:45:51Z","timestamp":1790246751070,"version":"4.1.0"},"reference-count":8,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[1960,7,1]],"date-time":"1960-07-01T00:00:00Z","timestamp":-299894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[1960,7,1]]},"abstract":"<jats:title>SUMMARY<\/jats:title>\n                  <jats:p>Estimation of statistical parameters from multivariate data results in wasted information, if units with incomplete data are rejected entirely, and perhaps in inconsistencies in the variance\u2013covariance matrix if the variances and correlation coefficients are estimated from all available data on individual variates and pairs of variates respectively. An alternative is to estimate the missing values by regression techniques and to calculate a revised variance\u2013covariance matrix. This method is suitable for use with an electronic computer. It is shown that with this method the resultant covariances are unbiased, but that the variances require correction for bias. A numerical example is given.<\/jats:p>","DOI":"10.1111\/j.2517-6161.1960.tb00375.x","type":"journal-article","created":{"date-parts":[[2018,12,7]],"date-time":"2018-12-07T01:43:38Z","timestamp":1544147018000},"page":"302-306","source":"Crossref","is-referenced-by-count":273,"title":["A Method of Estimation of Missing Values in Multivariate Data Suitable for Use with an Electronic Computer"],"prefix":"10.1093","volume":"22","author":[{"given":"S. F.","family":"Buck","sequence":"first","affiliation":[{"name":"Rothamsted Experimental Station"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2018,12,5]]},"reference":[{"key":"2024090921182937200_cit1","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1080\/01621459.1957.10501379","article-title":"Maximum likelihood estimates for a multivariate normal distribution when some observations are missing","volume":"52","author":"Anderson","year":"1957","journal-title":"J. Amer. Statist. Assoc."},{"key":"2024090921182937200_cit2","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1080\/01621459.1956.10501315","article-title":"Multiple regression with missing observations among the independent variables","volume":"51","author":"Edgett","year":"1956","journal-title":"J. Amer. Statist. Assoc."},{"key":"2024090921182937200_cit3","first-page":"145","article-title":"Estimation of parameters from incomplete data with application to design of sample survey","volume":"11","author":"Matthai","year":"1951","journal-title":"Sankhy\u0101"},{"key":"2024090921182937200_cit4","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1080\/01621459.1957.10501408","article-title":"Estimates of parameters from incomplete multivariate samples","volume":"52","author":"Nicholson","year":"1957","journal-title":"J. Amer. Statist. Assoc."},{"key":"2024090921182937200_cit5","volume-title":"Advanced Statistical Methods in Biometric Research","author":"Rao","year":"1956"},{"key":"2024090921182937200_cit6","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1214\/aoms\/1177732885","article-title":"Moments and distributions of estimates of population parameters from fragmentary samples","volume":"3","author":"Wilks","year":"1932","journal-title":"Ann. Math. 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