Flexible Imputation of Missing Data

By Hakan Demirtas. 2018

1,180 citations recorded by the source corpus.

Missingness is a commonly occurring phenomenon in many applications. Determining a suitable analytical approach in the absence of complete observations is a major focus of scientific inquiry due to the extra sophistication that arises through missing data. Incompleteness generally complicates the statistical analysis in terms of reduced statistical power, biased parameter estimates, and degraded confidence intervals, and thereby may lead to false inferences. Developments in computational statistics have produced flexible missing-data procedures with a sound statistical basis. One of these procedures involves multiple imputation (MI), which is a stochastic simulation technique in which the missing values are replaced by m > 1 simulated versions. Subsequently, each of the simulated complete data sets is analyzed by standard methods, and the results are combined into a single inferential statement that formally incorporates missing-data uncertainty to the modeling process. MI has gained widespread acceptance and popularity in the last few decades. It has some well-accepted advantages: First, MI allows researchers to use conventional models and software; an imputed data set may be analyzed by literally any method that would be appropriate if the data were complete. As computing environments and statistical models grow increasingly complex, the value of using familiar methods and software becomes more pronounced. Second, there are still many classes of problems for which no direct maximum likelihood procedure is available. Even when such a procedure exists, MI can be more attractive due to fact that the separation of the imputation phase from the analysis phase lends greater flexibility to the entire process. Lastly, MI singles out missing data as a source of random variation distinct from ordinary sampling variability.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/8d02b4acbb03127da809a6ccb3378f435edc062a

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Citations in the corpus

A survey on missing data in machine learning (2021, background): “Uni-variate: Missing data pattern is uni-variate when there is only one variable with missing data [25].”
Machine learning based early warning system enables accurate mortality risk prediction for COVID-19 (2020, background): “Imputation of clinical data with RF has been widely adopted 34,35 , which displayed the capability of handling mix-type missing values including continuous and categorical variables.”
Handling missing values in trait data (2020, positive): “If the objective of the imputation is to produce estimates of missing values (for example, to fill gaps in a dataset), single imputation is considered most effective, because the stochastic draws in multiple imputation add error (Van Buuren, 2012).”
HeMIS: Hetero-Modal Image Segmentation (2016, background): “This problem is not new, and the subject of missing data analysis has spawned an immense literature in statistics (e.g. [22]).”
Missing data and multiple imputation in clinical epidemiological research (2017, positive): “How many imputed data sets? Traditionally, it has been suggested that three to five imputed datasets are sufficient.(3,26) The argument was that even with 50% missing information, five imputed data sets would produce point estimates that are 91% as efficient as those based on an infinite number of i”
The WHO World Mental Health Surveys International College Student Project: Prevalence and Distribution of Mental Disorders (2018, positive): “Item-level missing data in the completed surveys were imputed using the method of multiple imputation (MI) by chained equations (van Buuren, 2012).”