The phrase "addressing missing data" is correct and usable in written English.
It can be used in contexts related to data analysis, research, or statistics when discussing how to handle or resolve gaps in data sets.
Example: "In our study, we focused on addressing missing data to ensure the accuracy of our results."
Alternatives: "tackling absent data" or "resolving data gaps".
The second approach employed for handling addressing missing data is through GSEM's maximum likelihood estimation procedures in Stata 13.
Statisticians have long pointed out that the use of listwise deletion is a poor approach to addressing missing data.
The benefits and drawbacks of various options for addressing missing data likely vary across studies; hence investigators will need to make decisions best suited to their studies.
In summary, a review of these issues and potential approaches to addressing missing data is necessary before beginning any subsequent analyses.
However, understanding the missingness structure can help lead the researcher to create better imputation models or use alternative methods of addressing missing data, as well as improve future data collection or conduct their own further investigations into missingness structure.
Second, while the primary goal of this study was to compare approaches to modelling the BMI-mortality relation using three important covariables, the complete case approach to addressing missing data and the omission of other potentially important explanatory variables may have introduced biases in parameter estimates.
Data preparation: Data processing should be reported, including the assessment of multivariate normality, analysis of missing data, method to address missing data, and data transformations.
In randomized trials, the primary analysis should be consistent with the intention-to-treat (ITT) principle and should address missing data appropriately to draw valid inferences.
We need to systematically address missing data values, outlier detection, feature subset selection and the continuous evolution of predictions as the project unfolds, and all of this in the context of data-starvation and noisy data.
Expectation Maximization (EM) algorithm is another powerful tool to address missing data problems.
Inverse probability of observation weighting was combined with multiple imputation to address missing data.
