A survey on missing data in machine learning
Abstract Machine learning has been the corner stone in analysing and extracting information
from data and often a problem of missing values is encountered. Missing values occur …
from data and often a problem of missing values is encountered. Missing values occur …
Missing data: An update on the state of the art.
CK Enders - Psychological Methods, 2023 - psycnet.apa.org
The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled
“Missing data: Our view of the state of the art,” currently the most highly cited paper in the …
“Missing data: Our view of the state of the art,” currently the most highly cited paper in the …
A comparison of multiple imputation methods for missing data in longitudinal studies
Background Multiple imputation (MI) is now widely used to handle missing data in
longitudinal studies. Several MI techniques have been proposed to impute incomplete …
longitudinal studies. Several MI techniques have been proposed to impute incomplete …
Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges
Background In high-dimensional data (HDD) settings, the number of variables associated
with each observation is very large. Prominent examples of HDD in biomedical research …
with each observation is very large. Prominent examples of HDD in biomedical research …
Multiple imputation using nearest neighbor methods
S Faisal, G Tutz - Information Sciences, 2021 - Elsevier
Missing values are a major problem in medical research. As the complete case analysis
discards useful information, estimation and inference may suffer strongly. Multiple imputation …
discards useful information, estimation and inference may suffer strongly. Multiple imputation …
Multiple imputation for multilevel data with continuous and binary variables
Supplement to “Multiple Imputation for Multilevel Data with Continuous and Binary
Variables”. Technical details on the posterior distributions of imputation model parameters …
Variables”. Technical details on the posterior distributions of imputation model parameters …
[PDF][PDF] Multiple imputation by chained equations in praxis: guidelines and review
JN Wulff, LE Jeppesen - Electronic Journal of Business Research …, 2017 - vbn.aau.dk
Multiple imputation by chained equations (MICE) is an effective tool to handle missing data-
an almost unavoidable problem in quantitative data analysis. However, despite the empirical …
an almost unavoidable problem in quantitative data analysis. However, despite the empirical …
Multiple imputation for general missing data patterns in the presence of high-dimensional data
Multiple imputation (MI) has been widely used for handling missing data in biomedical
research. In the presence of high-dimensional data, regularized regression has been used …
research. In the presence of high-dimensional data, regularized regression has been used …
SuperMICE: an ensemble machine learning approach to multiple imputation by chained equations
HS Laqueur, AB Shev… - American journal of …, 2022 - academic.oup.com
Researchers often face the problem of how to address missing data. Multiple imputation is a
popular approach, with multiple imputation by chained equations (MICE) being among the …
popular approach, with multiple imputation by chained equations (MICE) being among the …
Multiple imputation via generative adversarial network for high-dimensional blockwise missing value problems
Missing data are present in most real world problems and need careful handling to preserve
the prediction accuracy and statistical consistency in the downstream analysis. As the gold …
the prediction accuracy and statistical consistency in the downstream analysis. As the gold …