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Empirical comparison of imputation methods for missing values in data
Ostrenska, Alona ; Holý, Vladimír (advisor) ; Zouhar, Jan (referee)
Missing values are present in all types of data such as different surveys, socio-scientific information etc. In many applications, it is necessary to replace missing observations to maintain the size of the dataset needed for the statistics. This bachelor thesis at first place introduce the categories of causes of missing data and the problems connected with them. The next step is to acquaint with common methods of imputation of missing values and the explanation of applicating those methods on real data in the context of linear regression. Then the assumptions of linear regression models that are based on data with artificially created missing observations are verified. These observations are removed using the mentioned mechanisms and different proportion of missing, with seven subsequent imputation methods. Regression models constructed based on such imputed datasets are then statically verified. Finally, imputation models are compared using different statistics and visualizations and is suggested possible solution - particular methods in case of a real problem of incomplete data.

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