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Imputation (statistics)

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

Expectation–maximization algorithm

In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where t

Missing data

In statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation. Missing data are a common occurrence and can have a significant effect on the con

Listwise deletion

In statistics, listwise deletion is a method for handling missing data. In this method, an entire record is excluded from analysis if any single value is missing.

Geo-imputation

In data analysis involving geographical locations, geo-imputation or geographical imputation methods are steps taken to replace missing values for exact locations with approximate locations derived fr

Proportional reduction in loss

Proportional reduction in loss (PRL) is a general framework for developing and evaluating measures of the reliability of particular ways of making observations which are possibly subject to errors of

Horvitz–Thompson estimator

In statistics, the Horvitz–Thompson estimator, named after Daniel G. Horvitz and Donovan J. Thompson, is a method for estimating the total and mean of a in a stratified sample. Inverse probability wei

Predictive mean matching

Predictive mean matching (PMM) is a widely used statistical imputation method for missing values, first proposed by Donald B. Rubin in 1986 and R. J. A. Little in 1988. It aims to reduce the bias intr

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