Regression Estimation Following the Square-Root Transformation of the Response

$29.50 plus tax (Refund Policy)

Buy Article:

Abstract:

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.

Keywords: back-transformation bias; nonlinearity

Document Type: Research Article

Publication date: December 1, 2008

More about this publication?
  • Membership Information
  • ingentaconnect is not responsible for the content or availability of external websites
Related content

Tools

Favourites

Share Content

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
ingentaconnect 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