Should we impute or should we weight? Examining the performance of two CART-based techniques for addressing missing data in small sample research with nonnormal variables Article

Hayes, Timothy, McArdle, John J. (2017). Should we impute or should we weight? Examining the performance of two CART-based techniques for addressing missing data in small sample research with nonnormal variables . COMPUTATIONAL STATISTICS & DATA ANALYSIS, 115 35-52. 10.1016/j.csda.2017.05.006

cited authors

  • Hayes, Timothy; McArdle, John J

sustainable development goals

authors

publication date

  • November 1, 2017

published in

keywords

  • CART
  • Classification and regression trees
  • Computer Science
  • Computer Science, Interdisciplinary Applications
  • IMPUTATION
  • INFERENCE
  • MICE
  • MODELS
  • Mathematics
  • Missing data
  • Nonnormality
  • Physical Sciences
  • R PACKAGE
  • REGRESSION
  • Random forests
  • Science & Technology
  • Small samples
  • Statistics & Probability
  • Technology

Digital Object Identifier (DOI)

publisher

  • ELSEVIER

start page

  • 35

end page

  • 52

volume

  • 115