Abstract
Missing values are almost unavoidable in many practical data analyses. Particularly in the analysis of experimental data, since the number of observations is generally not so large, the influence of missing should be carefully checked. This paper discusses several graphical methods which are useful in assessing the effect of missing values in the analysis of variance of randomized block design. Some techniques such as simple imputation and multiple imputation have been proposed in the literature to cope with the missing values. However, with the use of recent computer facilities, more effective methods which utilize graphical representation seem to be called for. Techniques considered in this paper are functional representations of F-ratios and P-values against imputed values and graphical representations of the result of Monte Carlo simulation as an approximation to the Bayesian posterior distribution of missing value. Numerical examples are shown to illustrate the techniques. It is also claimed that the present methods can be used not only to assess the effect of missing values but also to evaluate the sensitiveness of obtained data.
