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- W1570385498 abstract "This chapter applies the tools to a variety of common problems involving incomplete data on multivariate normally distributed variables: estimation of the mean vector and covariance matrix; estimation of these quantities when there are restrictions on the mean and covariance matrix; multiple linear regression, including analysis of variance (ANOVA) and multivariate regression; repeated measures models, including random coefficient regression models where the coefficients themselves are regarded as missing data; and selected time series models. It discusses the maximum likelihood (ML) estimation of the mean and covariance matrix from an incomplete multivariate normal sample, assuming the missing-data mechanism is ignorable. Although the assumption of multivariate normality may appear restrictive, the methods discussed here can provide consistent estimates under weaker assumptions about the underlying distribution. The link between ML estimation and an efficient form of imputation for the missing values is clear from the EM algorithm." @default.
- W1570385498 created "2016-06-24" @default.
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- W1570385498 date "2002-08-26" @default.
- W1570385498 modified "2023-10-10" @default.
- W1570385498 title "Multivariate Normal Examples, Ignoring the Missing‐Data Mechanism" @default.
- W1570385498 doi "https://doi.org/10.1002/9781119013563.ch11" @default.
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