What is Data Imputation? Data Imputation Explained.
Data imputation, also known as missing data imputation, is the process of estimating or filling in missing values in a dataset. Missing data can occur due to various reasons such as data collection errors, equipment malfunctions, survey non-responses, or data corruption. Data imputation techniques aim to replace missing values with plausible estimates based on the available data.
Here are some common approaches to data imputation:
Mean/Median Imputation: Missing values in a variable are replaced with the mean or median value of that variable. This method assumes that the missing values are missing completely at random (MCAR) and that the distribution of the variable is roughly symmetric. Mean imputation preserves the mean of the variable, while median imputation preserves the median.
Regression Imputation: Missing values are estimated by regressing the variable with missing values on other variables in the dataset. A regression model is built using the complete cases, and then the model is used to predict the missing values based on the values of other variables. This method assumes that the missing values have a relationship with the other variables.
Hot-Deck Imputation: Hot-deck imputation involves filling in missing values by randomly selecting values from similar observations in the dataset. Similarity can be defined based on various criteria such as distance metrics or nearest neighbors. Hot-deck imputation attempts to preserve the relationships between variables.
Multiple Imputation: Multiple imputations generates multiple plausible imputed datasets by simulating the missing values based on a statistical model. Each imputed dataset is analyzed separately, and the results are combined using specific rules to obtain valid statistical inferences. Multiple imputation accounts for the uncertainty associated with the imputation process.
K-Nearest Neighbors (KNN) Imputation: KNN imputation replaces missing values with the values of the K most similar observations in the dataset. Similarity is typically measured using distance metrics, such as Euclidean distance or cosine similarity. KNN imputation preserves local patterns in the data.
Expectation-Maximization (EM) Algorithm: The EM algorithm is an iterative algorithm that estimates missing values by maximizing the likelihood of the observed data. It assumes that the data are missing at random (MAR) and iteratively imputes the missing values based on the available data and the estimated distribution parameters.
Domain-Specific Imputation: In some cases, domain-specific knowledge or external sources can be used to impute missing values. For example, historical data, expert opinions, or external databases can provide insights for imputing missing values in a meaningful way.
When performing data imputation, it is essential to consider the assumptions and limitations of each imputation method. Imputed values may introduce bias or impact the variability of the dataset, potentially affecting subsequent analyses or modeling. Therefore, it is recommended to evaluate the imputation quality, assess the impact on the results, and consider sensitivity analyses to account for the uncertainty introduced by imputation.
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.