# Multiple Imputation methods

Suppose that a variable $Y_j$ has missing values. We can use regression to impute the data using the nonmissing observations:

$$Y_j = \beta_0+\beta_{1}Y_{1}+\beta_{2}Y_{2} + \dots + \beta_{(j-1)} Y_{(j-1)}$$

What are the disadvantages of this? When would it be better to impute the data from a distribution? For example, if a variable  age  has missing values, when would it be better to randomly sample from the overall distribution of the non-missing values of  age ?