Comparing the Logarithmic Transformation and the Box-Cox Transformation for Individual Tree Basal Area Increment Models

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

Properties of the Box-Cox transformation for pattern classification

The Box–Cox transformation [1,2] (Box and Cox, 1964; Sakia, 1992) has been regarded as a parametric pre-processing technique aimed at making the distribution of a set of points approximately Gaussian. Since normality represents an assumption underlying many statistical data analysis tools, such technique has been widely applied in different fields of Computer Science. In this paper we will prov...

Assessing model performance in forecasting long-term individual tree diameter versus basal area increment for the primary Acadian tree species

Tree basal area (ba) or diameter at breast height (dbh) are universally used to represent tree secondary growth in individual tree based growth models. However, the long-term implications of using either ba or dbh for predictions are rarely fully assessed. In this analysis, Dba and Ddbh increment equations were fit to identical datasets gathered from six conifer and four hardwood species grown ...

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...

Improving your data transformations: Applying the Box-Cox transformation

Many of us in the social sciences deal with data that do not conform to assumptions of normality and/or homoscedasticity/homogeneity of variance. Some research has shown that parametric tests (e.g., multiple regression, ANOVA) can be robust to modest violations of these assumptions. Yet the reality is that almost all analyses (even nonparametric tests) benefit from improved the normality of var...