Imputation of Mixed Data With Multilevel Singular Value Decomposition
From MaRDI portal
Publication:3391265
DOI10.1080/10618600.2019.1585261OpenAlexW2963198273WikidataQ128239515 ScholiaQ128239515MaRDI QIDQ3391265
François Husson, Julie Josse, Geneviève Robin, Balasubramanian Narasimhan
Publication date: 28 March 2022
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1804.11087
matrix completionhierarchical datalow-rank matrix estimationdistributed computationsystematically and sporadically missing values
Related Items
Random effect models for multivariate mixed data: A Parafac-based finite mixture approach ⋮ A Bayesian Singular Value Decomposition Procedure for Missing Data Imputation ⋮ Application of iterative hybrid MI approach to household survey data with complex dependence structures ⋮ Matrix factorization for multivariate time series analysis ⋮ Main effects and interactions in mixed and incomplete data frames
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares
- Handling missing values with regularized iterative multiple correspondence analysis
- Adaptive shrinkage of singular values
- MIMCA: multiple imputation for categorical variables with multiple correspondence analysis
- Multiple imputation: a review of practical and theoretical findings
- Multiple imputation for multilevel data with continuous and binary variables
- A principal component method to impute missing values for mixed data
- Noisy low-rank matrix completion with general sampling distribution
- Exploratory Multivariate Analysis by Example Using R
- Why Are Big Data Matrices Approximately Low Rank?
- Optimal Shrinkage of Singular Values
- The Power of Convex Relaxation: Near-Optimal Matrix Completion
- Regularised PCA to denoise and visualise data
