Box and Cox power-transformation to additivity and homoscedasticity in regression

Box-Cox Transformation: An Overview Box-Cox Transformations: An Overview

Implementing Box-Cox Quantile Regression∗

The Box-Cox quantile regression model introduced by Powell (1991) is a flexible and numerically attractive extension of linear quantile regression techniques. Chamberlain (1994) and Buchinsky (1995) suggest a two stage estimator for this model but the objective function in stage two of their method may not be defined in an application. We suggest a modification of the estimator which is easy to...

Box–Cox Transformation and Random Regression Models for Fecal egg Count Data

Accurate genetic evaluation of livestock is based on appropriate modeling of phenotypic measurements. In ruminants, fecal egg count (FEC) is commonly used to measure resistance to nematodes. FEC values are not normally distributed and logarithmic transformations have been used in an effort to achieve normality before analysis. However, the transformed data are often still not normally distribut...

A new approach to the Box–Cox transformation

We propose a new methodology to estimate λ, the parameter of the Box–Cox transformation, as well as an alternative method to determine plausible values for it. The former is accomplished by defining a grid of values for λ and further perform a normality test on the λ-transformed data. The optimum value of λ, say ∗ λ , is such that the p-value from the normality test is the highest. The set of p...

Some Alternatives to the Box- Cox Regression Model

A nonlinear regression model is proposed as an alternative to the Box-Cox regression model for nonnegative variables. The functional form contains as special cases the linear, exponential, constant elasticity, and generalized CES specifications, as well as other functional forms used by applied econometricians . The model can be derived from but is more general than a particular modification of...