We deal with the effect of missing data under a ”Missing at Random Model” on classification of variables with non hierarchical methods. The partitions are compared by the Rand’s index.

We study the problem of maximum likelihood estimation for general patterns of bivariate missing data for normal and multinomial random variables, under the assumption that the data is missing at random (MAR). For normal data, the score equations have nine complex solutions, at least one of which is real and statistically significant. Our computations suggest that the number of real solutions is...

Background: The issue of missing data is prevalent in all type research work, which can diminish statistical power and lead to inaccurate results if not managed correctly. Missing cannot be ignored because every piece data, no matter how small, affects the outcome significantly. Imputation a key component dealing with data; however, best way impute values has yet been identified. Methods: Our g...

The databases store data that may be subjected to missing values either in data acquisition or data storage process. The proposed approach uses the widely used optimization technique called genetic algorithm for the NMAR (Not Missing At Random) missing mechanism which prevails more in real life that are non-ignorable. Since the non-ignorable mechanism needs prior