A Neuro-Based Software Fault Prediction with Box-Cox Power Transformation

A Neuro-Based Software Fault Prediction with Box-Cox Power Transformation

Software fault prediction is one of the most fundamental but significant management techniques in software dependability assessment. In this paper we concern the software fault prediction using a multilayer-perceptron neural network, where the underlying software fault count data are transformed to the Gaussian data, by means of the well-known Box-Cox power transformation. More specially, we in...

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

A random effects meta-analysis model with Box-Cox transformation

BACKGROUND In a random effects meta-analysis model, true treatment effects for each study are routinely assumed to follow a normal distribution. However, normality is a restrictive assumption and the misspecification of the random effects distribution may result in a misleading estimate of overall mean for the treatment effect, an inappropriate quantification of heterogeneity across studies and...

Accelerated Life Testing Model Building with Box-cox Transformation

In accelerated life testing, the nominal life time is often related to stress levels by an acceleration equation. Three particular models that have been used frequently in the past are the power law model, the Arrhenius model and the Eyring model. In this paper we suggest choosing a model from a model family which includes the three particular models as special cases. This family is deened by a...

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...