Estimation
of Missing Data, Imputation and Test Statistics in Two-Way Classification Mixed
Models
Diana Carolina Franco & Oscar Orlando Melo
We propose a methodology to estimate
missing information in mixed cell means models. This methodology improves on
that Melo & Melo (2005), which is based on the methods of maximum likelihood
estimation and covariate proposed by (Bartlett 1937), and reduces the
correlation between the observed and estimated information. Once the imputation
of the missing information is done, we suggest a way to perform the analysis of
variance in models without interaction, by generating a weighted test for the
fixed and random effects involved in the model.
Key words: Cell means model, Mixed model,
Missing information, Estimation and imputation, Distribution of quadratic
forms.
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