The following pages link to Missing data (Q2761082):
Displaying 30 items.
- Ternary Bradley-Terry model-based decoding for multi-class classification and its extensions (Q415622) (← links)
- MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (Q518262) (← links)
- Analysis of NMAR missing data without specifying missing-data mechanisms in a linear latent variate model (Q549917) (← links)
- The 2005 Plenary Meeting on ``Missing data and measurement error'' (Q878276) (← links)
- A modified rough set approach to incomplete information systems (Q933872) (← links)
- Identifying variables responsible for data not missing at random (Q1029514) (← links)
- Principal component analysis with interval imputed missing values (Q1633223) (← links)
- Navigating choices when applying multiple imputation in the presence of multi-level categorical interaction effects (Q1731416) (← links)
- Class noise vs. attribute noise: A quantitative study of their impacts (Q1768897) (← links)
- Multiple imputation for multilevel data with continuous and binary variables (Q1799343) (← links)
- A new robust ratio estimator by modified Cook's distance for missing data imputation (Q2103294) (← links)
- The case for the use of multiple imputation missing data methods in stochastic frontier analysis with illustration using English local highway data (Q2272300) (← links)
- Robust regression with compositional covariates including cellwise outliers (Q2673343) (← links)
- Weighted Optimization with Thresholding for Complete-Case Analysis (Q3296452) (← links)
- Mixtures of regression models with incomplete and noisy data (Q4563423) (← links)
- Generating Incomplete Data with DataZapper (Q4932619) (← links)
- Symbolic missing data imputation in principal component analysis (Q4969759) (← links)
- Graphical Models for Processing Missing Data (Q4999179) (← links)
- (Q5093545) (← links)
- The estimation of<i>R</i><sup>2</sup>and adjusted<i>R</i><sup>2</sup>in incomplete data sets using multiple imputation (Q5123407) (← links)
- Correction of Bias in Imputing Missing Values of Categorical Variables (Q5190611) (← links)
- Missing Data Restoration Algorithm (Q5262415) (← links)
- An Examination of Discrepancies in Multiple Imputation Procedures Between SAS® and SPSS® (Q5868168) (← links)
- Fitting feature-dependent Markov chains (Q6064024) (← links)
- Evaluation of approaches for accommodating interactions and non‐linear terms in multiple imputation of incomplete three‐level data (Q6068869) (← links)
- Multiple imputation regression discontinuity designs: Alternative to regression discontinuity designs to estimate the local average treatment effect at the cutoff (Q6082994) (← links)
- Ignoring non-ignorable missingness (Q6160306) (← links)
- A longitudinal transition imputation model for categorical data applied to a large registry dataset (Q6560542) (← links)
- Effect of data preprocessing on ensemble learning for classification in disease diagnosis (Q6562718) (← links)
- Multiple imputation of semi-continuous exposure variables that are categorized for analysis (Q6628314) (← links)
