ESTIMATING THE MISSING VALUES IN ANALYSIS OF VARIANCE TABLES BY A FLEXIBLE ADAPTIVE ARTIFICIAL NEURAL NETWORK AND FUZZY REGRESSION MODELS

Missing data are a part of almost all research, and we all have to decide how to deal with it from time to time. There are a number of alternative ways of dealing with missing data. The problem of handling missing data has been treated adequately in various real world data sets. Several statistical methods have been developed since the early 1970s, when the manipulation of complicated numerical calculations became feasible with the advance of computers. The purpose of this research is to estimate missing values by using artificial neural network (ANN), fuzzy regression models, and approach in a complete randomized block design table (analysis of variance) and to compare the computational results with two other methods, namely the approximate analysis and exact regression method. It is concluded that ANN provides much better estimation than the conventional approaches. The superiority of ANN is shown through lower error estimations.