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The essence of hot deck imputation is that missing data in a recipient file (TALIS) are filled in with actual values from a donor data file (PISA) based on a pre‐specified algorithm.
In this case, the recipient file is not a necessary representative of the population.
Our aim is to complete the information of the recipient file in order to take advantage of the existing relationship between the donor and recipient data.
Here, we address the problem in its simplest case, called unilateral fusion (Fig. 1), in which there are two files: one with X and Y variables (donor file D: X0; Y0), and the other with only X variables (recipient file R: X1).
The goal is to complete the recipient file \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$X_{ 1},\hat {Y}_{1})$\end{document} (X 1, Ŷ 1 ) in such a way that it can a be an instance of f X, Y).
The second approach consists of directly modellingthe relationship between the Y and X variables in the donor file D by means of a regression function, E(Y| X)= r(X)+ ε, and then applying this model to the recipient file R (explicit modelling).
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That program became controversial after Solyndra, the first solar recipient, filed for bankruptcy, leaving taxpayers potentially liable for more than $500 million.
Extract bootstrap samples from the donor and recipient files.
It assumes a common distribution f X, Y| θ) for the donor and recipient files.
Let n0 and n1 be the sizes of the donor and recipient files respectively.
In fact, recipient files may not be a representative sample at all [ 4].
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