Simulation for Data Science with R

Proper variance estimation with missing values

Very often in practice, missing values are a major problem. Standard routines for estimation are typically not designed to deal with missing values. In the following we discuss a method to adequately deal with missing values when estimating the variance/uncertainty of an estimator.

Because of non-answered questions or measurement errors, data often has the following data structure:

Here we see n observations and p variables and some missing values (NA).

Often one will omit those observations that include missing values from the data set. However, this decreases the sample size and thus increases the variance ...