Skip to main content

Regression Estimation Following the Square-Root Transformation of the Response

The full text article is temporarily unavailable.

We apologise for the inconvenience. Please try again later.

In a variety of regression situations, there is interest in predicting the value of Y 2, yet it is useful to model it using a square root transformation, such that Y rather than Y 2 is regressed on one or more covariates. The back-transformation bias of the square root transformation of the response variable of interest is presented in detail. An unbiased estimator is presented: Ê[Y 2|x ] =  y|x 2 +  − V( y|x 2). Its performance is compared against that of two biased estimators: Êb [Y 2|x ] =  y|x 2 +  and Êp [Y 2|x ] =  y|x 2. The first two moments of these estimators are derived analytically and verified by means of a simulation study. Both biased estimators have lower mean square errors than the unbiased estimator. An example wherein aboveground biomass is the response variable is presented for illustration.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: back-transformation bias; nonlinearity

Document Type: Research Article

Publication date: 01 October 2008

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more