A Comparison of Different Methods for Estimating the Missing Value in Two-way ANOVA
In this study, we consider missing value problem in two-way ANOVA model under the non-normal error terms, namely long-tailed symmetric (LTS) distribution. We obtain estimators of the missing value by using two different methodologies. First, iteratively reweighting algorithm (IRA) is used to compute the maximum likelihood (ML) estimate of the missing value. Second, the modified maximum likelihood (MML) methodology is used to obtain the explicit estimator of the missing value. In the simulation study, we compare the efficiencies of the proposed estimators with the traditional least squares (LS) estimator. We also show the effects of the proposed estimators on the efficiencies of the estimators of the model parameters. We illustrate the estimation methods on a real life example taken from the literature at the end of the study.
Disclaimer/Regarding indexing issue:
We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information. Also: DOI is paid service which provided by a third party. We never mentioned that we go for this for our any journal. However, journal have no objection if author go directly for this paid DOI service.