Handling missing data of some skewed distributions /
معالجة البيانات المفقودة لبعض التوزيعات الملتوية
Mohamed Meatemed Mohamed Eissawy ; Supervised Ahmed Amin Elsheikh , Naglaa Abdelmoneim Morad
- Cairo : Mohamed Meatemed Mohamed Eissawy , 2015
- 102 Leaves : charts ; 30cm
Thesis (M.Sc.) - Cairo University - Institute of Statistical Studies and research - Department of Statistics and Econometrics
The problem of imputation of missing observations emerges in many areas. Data usually contained missing observations due to many factors, such as machine failures and human error. Incomplete dataset usually causes bias due to differences between observed and unobserved data. The treatment of missing data has been an issue in statistics for some time, but it has come to the fore in recent years. The current interest in missing data stems mostly from the problems caused in surveys and census data, but the topic is actually much broader than that. Imputation is an increasingly popular method for handling data with missing values. When using imputation, analysts fill in missing values with random draws from an imputation model, and then fit the imputed data to an analysis model. In an ideal world, the imputation model would perfectly represent the distribution of the data. But such perfect fidelity can be very difficult to achieve, and in practice it is often unnecessary. All that is necessary is that the imputation model preserves those aspects of the distribution that are relevant to the analysis model. The point of imputation is not that the imputed values should look like observed values. The point is that the imputed variable should act like the observed variable when used in analysis. Researchers often impute continuous variables under an assumption of normality, yet many incomplete variables are skewed. It was found that imputing skewed variables under a normal model can lead to bias