Inference for general parametric functions in box-Cox-type transformation models

Simultaneous inference in general parametric models.

Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describ...

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...

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 class of Box-Cox transformation models for recurrent event data.

In this article, we propose a class of Box-Cox transformation models for recurrent event data, which includes the proportional means models as special cases. The new model offers great flexibility in formulating the effects of covariates on the mean functions of counting processes while leaving the stochastic structure completely unspecified. For the inference on the proposed models, we apply a...