R. Little. Journal of Business and Economic Statistics, 6 (3):
287-301(1988)
Abstract
Useful properties of a general-purpose imputation method for numerical
data are suggested and discussed in the context of several large
government surveys. Imputation based on predictive mean matching
is proposed as a useful extension of methods in existing practice,
and versions of the method are presented for unit nonresponse and
item nonresponse with a general pattern of missingness. Extensions
of the method to provide multiple imputations are also considered.
Pros and cons of weighting adjustments are discussed, and weighting-based
analogs to pre- dictive mean matching are outlined
%0 Journal Article
%1 Little1988
%A Little, Roderick J.A.
%D 1988
%J Journal of Business and Economic Statistics
%K Imputation; Incomplete Matching; Multiple Regression Weighting data; imputation; models;
%N 3
%P 287-301
%T Missing-data adjustments in large surveys
%V 6
%X Useful properties of a general-purpose imputation method for numerical
data are suggested and discussed in the context of several large
government surveys. Imputation based on predictive mean matching
is proposed as a useful extension of methods in existing practice,
and versions of the method are presented for unit nonresponse and
item nonresponse with a general pattern of missingness. Extensions
of the method to provide multiple imputations are also considered.
Pros and cons of weighting adjustments are discussed, and weighting-based
analogs to pre- dictive mean matching are outlined
@article{Little1988,
abstract = {Useful properties of a general-purpose imputation method for numerical
data are suggested and discussed in the context of several large
government surveys. Imputation based on predictive mean matching
is proposed as a useful extension of methods in existing practice,
and versions of the method are presented for unit nonresponse and
item nonresponse with a general pattern of missingness. Extensions
of the method to provide multiple imputations are also considered.
Pros and cons of weighting adjustments are discussed, and weighting-based
analogs to pre- dictive mean matching are outlined},
added-at = {2010-07-07T17:27:19.000+0200},
author = {Little, Roderick J.A.},
biburl = {https://www.bibsonomy.org/bibtex/2bdd8c5e6539a510ad69f507679f31655/pillo},
comment = {coins term: predictive mean matching},
interhash = {6d592b2ac516d42d2b8a865bb945d2d9},
intrahash = {bdd8c5e6539a510ad69f507679f31655},
journal = {Journal of Business and Economic Statistics},
keywords = {Imputation; Incomplete Matching; Multiple Regression Weighting data; imputation; models;},
number = 3,
owner = {Bernd Panassiti},
pages = {287-301},
timestamp = {2010-07-07T17:27:22.000+0200},
title = {Missing-data adjustments in large surveys},
volume = 6,
year = 1988
}