A numerical study of multiple imputation methods using nonparametric multivariate outlier identifiers and depth-based performance criteria with clinical laboratory data
From MaRDI portal
Publication:3019808
DOI10.1080/00949650903437842zbMath1221.62100OpenAlexW2074879932MaRDI QIDQ3019808
Publication date: 29 July 2011
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00949650903437842
Multivariate analysis (62H99) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric inference (62G99)
Cites Work
- Unnamed Item
- Unnamed Item
- Nonparametric depth-based multivariate outlier identifiers, and masking robustness properties
- Projection-based depth functions and associated medians
- General notions of statistical depth function.
- Principal component analysis.
- Multiple outlier detection in multivariate data using projection pursuit techniques
- Equivariance and invariance properties of multivariate quantile and related functions, and the role of standardisation
- Multiple Imputation After 18+ Years
- On a Geometric Notion of Quantiles for Multivariate Data
- Least Median of Squares Regression
- Fully conditional specification in multivariate imputation
- The multivariate L 1 -median and associated data depth
- Multiple imputation of discrete and continuous data by fully conditional specification
- On affine equivariant multivariate quantiles
