Multiple Imputation Methods for Multivariate One-Sided Tests with Missing Data

Subscribe to email list

Please select the email list(s) to which you wish to subscribe.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Image CAPTCHA

Enter the characters shown in the image.

User menu

You are here

  1. Department of Statistics
  2. Multiple Imputation Methods for Multivariate One-Sided Tests with Missing Data

Multiple Imputation Methods for Multivariate One-Sided Tests with Missing Data

TitleMultiple Imputation Methods for Multivariate One-Sided Tests with Missing Data
Publication TypeJournal Article
Year of Publication2011
AuthorsWang, T, WU, LANG
JournalBiometrics
Volume67
Pagination1452–1460
Date Publisheddec
ISSN1541-0420
KeywordsConstrained inference, Multiple imputation, Order-restricted inference, Wald-type tests
AbstractSummary Multivariate one-sided hypotheses testing problems arise frequently in practice. Various tests have been developed. In practice, there are often missing values in multivariate data. In this case, standard testing procedures based on complete data may not be applicable or may perform poorly if the missing data are discarded. In this article, we propose several multiple imputation methods for multivariate one-sided testing problem with missing data. Some theoretical results are presented. The proposed methods are evaluated using simulations. A real data example is presented to illustrate the methods.
URLhttp://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2011.01597.x/abstract
DOI10.1111/j.1541-0420.2011.01597.x