  Fractional imputation (FI) is a relatively new method of imputation for
handling item nonresponse in survey sampling. In FI, several imputed values
with their fractional weights are created for each missing item. Each
fractional weight represents the conditional probability of the imputed value
given the observed data, and the parameters in the conditional probabilities
are often computed by an iterative method such as EM algorithm. The underlying
model for FI can be fully parametric, semiparametric, or nonparametric,
depending on plausibility of assumptions and the data structure.
  In this paper, we give an overview of FI, introduce key ideas and methods to
readers who are new to the FI literature, and highlight some new development.
We also provide guidance on practical implementation of FI and valid
inferential tools after imputation. We demonstrate the empirical performance of
FI with respect to multiple imputation using a pseudo finite population
generated from a sample in Monthly Retail Trade Survey in US Census Bureau.
