The Box-Cox power transformation family for non-negative responses in linear models has a long and interesting history in both statistical practice and theory, which we summarize. The relationship between generalized linear models and log transformed data is illustrated. Extensions investigated include the transform both sides model and the Yeo-Johnson transformation for observations that can be positive or negative. The paper also describes an extended Yeo-Johnson transformation that allows positive and negative responses to have different power transformations. Analyses of data show this to be necessary. Robustness enters in the fan plot for which the forward search provides an ordering of the data. Plausible transformations are checked w...
We analyse data on the performance of investment funds, 99 out of 309 of which report a loss, and on...
Although the normal probability distribution is the cornerstone of applying statistical methodology;...
Response transformations are a popular approach to adapt data to a linear regression model. The regr...
The Box-Cox power transformation family for non-negative responses in linear models has a long and i...
The Box-Cox power transformation family for non-negative responses in linear models has a long and ...
The paper introduces an automatic procedure for the parametric transformation of the response in reg...
Transformation of a response variable can greatly expand the class of problems for which the linear ...
The Box-Cox method has been widely used to improve estimation accuracy in different fields, especial...
Many of us in the social sciences deal with data that do not conform to assumptions of normality and...
Abstract. Box & Cox (1964) proposed a parametric power transformation technique in order to redu...
A few days ago, a former student of mine, David, came back to me about Box-Cox tests in linear model...
Copyright © 2014 Aboobacker Jahufer. This is an open access article distributed under the Creative C...
The paper introduces an automatic procedure for the parametric transformation of the response in reg...
We investigate power transformations in non-linear regression problems when there is a physical mode...
Although the normal probability distribution is the cornerstone of applying statistical methodology;...
We analyse data on the performance of investment funds, 99 out of 309 of which report a loss, and on...
Although the normal probability distribution is the cornerstone of applying statistical methodology;...
Response transformations are a popular approach to adapt data to a linear regression model. The regr...
The Box-Cox power transformation family for non-negative responses in linear models has a long and i...
The Box-Cox power transformation family for non-negative responses in linear models has a long and ...
The paper introduces an automatic procedure for the parametric transformation of the response in reg...
Transformation of a response variable can greatly expand the class of problems for which the linear ...
The Box-Cox method has been widely used to improve estimation accuracy in different fields, especial...
Many of us in the social sciences deal with data that do not conform to assumptions of normality and...
Abstract. Box & Cox (1964) proposed a parametric power transformation technique in order to redu...
A few days ago, a former student of mine, David, came back to me about Box-Cox tests in linear model...
Copyright © 2014 Aboobacker Jahufer. This is an open access article distributed under the Creative C...
The paper introduces an automatic procedure for the parametric transformation of the response in reg...
We investigate power transformations in non-linear regression problems when there is a physical mode...
Although the normal probability distribution is the cornerstone of applying statistical methodology;...
We analyse data on the performance of investment funds, 99 out of 309 of which report a loss, and on...
Although the normal probability distribution is the cornerstone of applying statistical methodology;...
Response transformations are a popular approach to adapt data to a linear regression model. The regr...