ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2018
An imputation-regularized optimization algorithm for high dimensional missing data problems and beyond.
Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
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- Deep network embedding with dimension selection.Neural networks : the official journal of the International Neural Network Society · 2024Article
- Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing.Statistics in medicine · 2024Article
- Causal-StoNet: Causal Inference for High-Dimensional Complex Data.... International Conference on Learning Representations · 2024Article
- Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems.Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications · 2021Article
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- Accelerate Training of Restricted Boltzmann Machines via Iterative Conditional Maximum Likelihood Estimation.Statistics and its interface · 2019Article
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- A Blockwise Consistency Method for Parameter Estimation of Complex Models.Sankhya. Series B. [Methodological.] · 2018Article
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Authors and funding
5 authors.
Funding
Abstract
Missing data are frequently encountered in high dimensional problems, but they are usually difficult to deal with by using standard algorithms, such as the expectation-maximization algorithm and its variants. To tackle this difficulty, some problem-specific algorithms have been developed in the literature, but there still lacks a general algorithm. This work is to fill the gap: we propose a general algorithm for high dimensional missing data problems. The algorithm works by iterating between an imputation step and a regularized optimization step. At the imputation step, the missing data are imputed conditionally on the observed data and the current estimates of parameters and, at the regularized optimization step, a consistent estimate is found via the regularization approach for the minimizer of a Kullback-Leibler divergence defined on the pseudocomplete data. For high dimensional problems, the consistent estimate can be found under sparsity constraints. The consistency of the averaged estimate for the true parameter can be established under quite general conditions. The algorithm is illustrated by using high dimensional Gaussian graphical models, high dimensional variable selection and a random-coefficient model.
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