Replication Data for: The MIDAS Touch: Accurate and Scalable Missing-Data Imputation with Deep Learning
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Replication and simulation reproduction materials for the article 'The MIDAS Touch: Accurate and Scalable Missing-Data Imputation with Deep Learning.'Please see the README file for a summary of the contents and the Replication Guide for a more detailed description.Article abstract:Principled methods for analyzing missing values, based chiefly on multiple imputation, have become increasingly popular yet can struggle to handle the kinds of large and complex data that are also becoming common. We propose an accurate, fast, and scalable approach to multiple imputation, which we call MIDAS (Multiple Imputation with Denoising Autoencoders). MIDAS employs a class of unsupervised neural networks known as denoising autoencoders, which are designed to reduce dimensionality by corrupting and attempting to reconstruct a subset of data. We repu' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["missing data","multiple imputation","machine learning","imputation methods"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Replication and simulation reproduction materials for the article 'The MIDAS Touch: Accurate and Scalable Missing-Data Imputation with Deep Learning.'Please see the README file for a summary of the contents and the Replication Guide for a more detailed description.Article abstract:Principled methods for analyzing missing values, based chiefly on multiple imputation, have become increasingly popular yet can struggle to handle the kinds of large and complex data that are also becoming common. We propose an accurate, fast, and scalable approach to multiple imputation, which we call MIDAS (Multiple Imputation with Denoising Autoencoders). MIDAS employs a class of unsupervised neural networks known as denoising autoencoders, which are designed to reduce dimensionality by corrupting and attempting to reconstruct a subset of data. We repu' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Replication and simulation reproduction materials for the article 'The MIDAS Touch: Accurate and Scalable Missing-Data Imputation with Deep Learning.'Please see the README file for a summary of the contents and the Replication Guide for a more detailed description.Article abstract:Principled methods for analyzing missing values, based chiefly on multiple imputation, have become increasingly popular yet can struggle to handle the kinds of large and complex data that are also becoming common. We propose an accurate, fast, and scalable approach to multiple imputation, which we call MIDAS (Multiple Imputation with Denoising Autoencoders). MIDAS employs a class of unsupervised neural networks known as denoising autoencoders, which are designed to reduce dimensionality by corrupting and attempting to reconstruct a subset of data. We repu
missing data; multiple imputation; machine learning; imputation methods