… to impute the missing data. The method … methods of handling missing data to establish the
most optimal method. The suitability is hence determined by the method whose imputed data …

… the topic of missing data. We introduce the main types of missing data that can occur in
practice and discuss the practical consequences of each of these types for general data analysis. …

… Next, we use an artificial data set to illustrate the application of these methods to some …
an overview of planned missing data designs. We use an artificial data set to illustrate key …

… on the study of methods of handling missing data in data mining. In this study, the overall
view of the method of handling missing data with statistical methods and machine learning is dis…

… a) Mean and Median Imputation: Even though there are a number of sophisticated and
complex imputation methods for dealing with the missing data, we have decided to deploy mean, …

… handle these inescapable missing values. This paper reviews some state-of-art practices
obtained in the literature for handling missing data … with missing data handling techniques were …

… to handle missing data problems. Missing data is particularly useful because many
statistical issues can be treated as special cases of the missing data problem. For example, …

… of existing methods for handle missing data on confounders … methods are valid. We focus
on situations where some variables are not recorded during the visit, rather than missing data …

… Missing data can introduce bias … missing data can mislead physicians and guideline
developers. In this commentary, we focus on discussing the implications of missing outcome data (…

… of data mining methods and traditional missing data … of different methods to handle
missing data when estimating … methods used for handling missing data in the current study. …