Title

Inference for General Parametric Functions in Box-Cox-Type Transformation Models

Publication Type

Journal Article

Publication Date

2008

Abstract

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]

Keywords

Box-Cox transformation, confidence interval, marginal effect, percentile function, robustness survivor function, test, variance inflation factor

Discipline

Econometrics | Medicine and Health Sciences

Research Areas

Econometrics

Publication

Canadian Journal of Statistics

Volume

36

Issue

2

First Page

301

ISSN

0319-5724

Identifier

10.1002/cjs.5550360208

Publisher

Statistical Science Association of Canada

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1002/cjs.5550360208

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