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In statistics, imputation is the process of replacing missing data with substituted values. When substituting for a data point, it is known as "unit imputation"; when substituting for a component of a data point, it is known as "item imputation".
本文介绍一种可利用整个数据集的方法——多重插补（Multiple Imputation, MI）。多重插补是一种处理缺失值的方法，它使用模型估计和重复模拟来生成一组完整的数据集。每个数据集中的缺失数据会通过估计模型的方法进行填补。
imputation. noun [ C or U ] formal uk / ˌɪm.pjuˈteɪ.ʃ ə n / us / ˌɪm.pjəˈteɪ.ʃ ə n /. Add to word list. Add to word list. a suggestion that someone is guilty of something or has a particular bad quality. 非難，詆毀，汙名. imputations of dishonesty 對其不誠實的指責. 同義詞.
6.4. Imputation of missing values ¶. For various reasons, many real world datasets contain missing values, often encoded as blanks, NaNs or other placeholders. Such datasets however are incompatible with scikit-learn estimators which assume that all values in an array are numerical, and that all have and hold meaning.
2023年8月29日 · Imputation is a technique used for replacing the missing data with some substitute value to retain most of the data/information of the dataset. These techniques are used because removing the data from the dataset every time is not feasible and can lead to a reduction in the size of the dataset to a large extend, which not only raises ...
2021年12月8日 · Imputation means replacing a missing value with another value based on a reasonable estimate. You use other data to recreate the missing value for a more complete dataset. You can choose from several imputation methods. The easiest method of imputation or
2020年10月22日 · Multiple imputation is blind to which variables are outcomes and which variables are predictors in the final analysis model. When developing the imputation models, the important issue is to include in the imputation models all of the variables from the analysis
2023年12月13日 · Understand the issue at hand through a real data set involving missing observations. Understand different classes of missing data and missing data mechanisms. Identify the types of missingness in the data. Learn the commonly used strategies for handling missing data and how to apply these strategies.
Missing value imputation (MVI) has been studied for several decades being the basic solution method for incomplete dataset problems, specifically those where some data samples contain one or more missing attribute values.
2015年4月7日 · Missing data are common in medical research, which can lead to a loss in statistical power and potentially biased results if not handled appropriately. Multiple imputation (MI) is a statistical method, widely adopted in practice, for dealing with missing data.
