Handling missing data in matched case‐control studies using multiple imputation

Missing data imputation in multivariable time series data

Multivariate time series data are found in a variety of fields such as bioinformatics, biology, genetics, astronomy, geography and finance. Many time series datasets contain missing data. Multivariate time series missing data imputation is a challenging topic and needs to be carefully considered before learning or predicting time series. Frequent researches have been done on the use of diffe...

Handling missing data: analysis of a challenging data set using multiple imputation

Multiple Imputation for Missing Data

Multiple imputation provides a useful strategy for dealing with data sets with missing values. Instead of filling in a single value for each missing value, Rubin’s (1987) multiple imputation procedure replaces each missing value with a set of plausible values that represent the uncertainty about the right value to impute. These multiply imputed data sets are then analyzed by using standard proc...

ST-14 Handling Missing Data with Multiple Imputation Using PROC MI in SAS

The multiple imputation was developed as a general method for inference with missing data. Instead replacing the missing observation with a single value, multiple imputation method replaces each missing value with multiple plausible values. PROC MI in SAS creates multiply imputed data sets for incomplete multivariate data. This study reviews multiple imputation as an analytic strategy for missi...

Handling Missing Data in Trees: Surrogate Splits or Statistical Imputation

In many applications of data mining a sometimes considerable part of the data values is missing. This may occur because the data values were simply never entered into the operational systems from which the mining table was constructed, or because for example simple domain checks indicate that entered values are incorrect. Despite the frequent occurrence of missing data, most data mining algorit...