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gcimpute: A Package for Missing Data Imputation
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DOI：10.18637/jss.v108.i04.png)
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Abstract
En 中文
This article introduces the Python package gcimpute for missing data imputation. Package gcimpute can impute missing data with many different variable types, including continuous, binary, ordinal, count, and truncated values, by modeling data as samples from a Gaussian copula model. This semiparametric model learns the marginal distribution of each variable to match the empirical distribution, yet describes the interactions between variables with a joint Gaussian that enables fast inference, imputation with confidence intervals, and multiple imputation. The package also provides specialized extensions to handle large datasets (with complexity linear in the number of observations) and streaming datasets (with online imputation). This article describes the underlying methodology and demonstrates how to use the software package.
Keywords:
missing data
single imputation
multiple imputation
Gaussian copula
mixed data
imputation uncertainty
Python
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8.1
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622
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