Publication

Resamping variance estimation in surveys with missing data

Anthony Christopher Davison, Sylvain Sardy
2007
Journal Articles

Abstract

We discuss variance estimation by resampling in surveys in which data are missing. We derive a formula for jackknife linearization in the case of calibrated estimation with deterministic regression imputation, and compare the resulting variance estimates with balanced repeated replication with and without grouping, the bootstrap, the block jackknife, and multiple imputation, for simulated data based on the Swiss Household Budget Survey. Jackknife linearisation, the bootstrap, and multiple imputation perform best in terms of relative bias and mean square error.

Official source

https://infoscience.epfl.ch/entities/publication/284124f9-3e4d-469d-ae07-0958a62b38e5

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Anthony Christopher Davison, Sylvain Sardy
2007
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/284124f9-3e4d-469d-ae07-0958a62b38e5

About this result

Related concepts (31)

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 conclusions that can be drawn from the data. Missing data can occur because of nonresponse: no information is provided for one or more items or for a whole unit ("subject"). Some items are more likely to generate a nonresponse than others: for example items about private subjects such as income.

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 data point, it is known as "item imputation". There are three main problems that missing data causes: missing data can introduce a substantial amount of bias, make the handling and analysis of the data more arduous, and create reductions in efficiency.

Variance

In probability theory and statistics, variance is the squared deviation from the mean of a random variable. The variance is also often defined as the square of the standard deviation. Variance is a measure of dispersion, meaning it is a measure of how far a set of numbers is spread out from their average value. It is the second central moment of a distribution, and the covariance of the random variable with itself, and it is often represented by , , , , or .

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