Inference for General Parametric Functions in Box-Cox-Type Transformation Models
The authors propose a simple but general method of inference for a parametric function of the Box-Cox-type transformation model. Their approach is built upon the classical normal theory but takes parameter estimation into account. It quickly leads to test statistics and confidence intervals for a linear combination of scaled or unscaled regression coefficients, as well as for the survivor function and marginal effects on the median or other quantile functions of an original response. The authors show through simulations that the finite-sample performance of their method is often superior to the delta method, and that their approach is robust to mild departures from normality of error distributions. They illustrate their approach with a numerical example. [PUBLICATION ABSTRACT]
Box-Cox transformation, confidence interval, marginal effect, percentile function, robustness survivor function, test, variance inflation factor
Econometrics | Medicine and Health Sciences
Canadian Journal of Statistics
Statistical Science Association of Canada
YANG, Zhenlin; Wu, E. K. H.; and Desmond, A. F..
Inference for General Parametric Functions in Box-Cox-Type Transformation Models. (2008). Canadian Journal of Statistics. 36, (2), 301. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/297
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
This document is currently not available here.