The Box-Cox transformation is a well known family of
power transformations that brings a set of data into agreement
with the normality assumption of the residuals and hence the
response variable of a postulated model in regression analysis. In
this paper we use six different data sets to implement adaptive
maximum likelihood Box-Cox transformation parameter
estimation in regression analysis. In addition, we perform
random permutation and Monte-Carlo simulation to investigate
the performances of the adaptive method.
%0 Journal Article
%1 noauthororeditor2016adaptive
%A Mezbahur Rahman, Reid W Breitenfeldt, Jinzhu Jiang
%D 2016
%J International Journal of Research and Innovation in Applied Science (IJRIAS)
%K ijrias, mathematics,
%N 4
%P 01-09
%T A Note on Adaptive Box-Cox Transformation Parameter in Linear Regression
%U http://www.ijrias.org/DigitalLibrary/Vol.1&Issue4/01-09.pdf
%V 1
%X The Box-Cox transformation is a well known family of
power transformations that brings a set of data into agreement
with the normality assumption of the residuals and hence the
response variable of a postulated model in regression analysis. In
this paper we use six different data sets to implement adaptive
maximum likelihood Box-Cox transformation parameter
estimation in regression analysis. In addition, we perform
random permutation and Monte-Carlo simulation to investigate
the performances of the adaptive method.
@article{noauthororeditor2016adaptive,
abstract = {The Box-Cox transformation is a well known family of
power transformations that brings a set of data into agreement
with the normality assumption of the residuals and hence the
response variable of a postulated model in regression analysis. In
this paper we use six different data sets to implement adaptive
maximum likelihood Box-Cox transformation parameter
estimation in regression analysis. In addition, we perform
random permutation and Monte-Carlo simulation to investigate
the performances of the adaptive method. },
added-at = {2016-10-13T10:18:13.000+0200},
author = {{Mezbahur Rahman, Reid W Breitenfeldt}, Jinzhu Jiang},
biburl = {https://www.bibsonomy.org/bibtex/2c15e01adc88dd704afdea70a3e84c58c/ijrias},
interhash = {6ee48f2c6df70faf15ce94ec6416931c},
intrahash = {c15e01adc88dd704afdea70a3e84c58c},
journal = {International Journal of Research and Innovation in Applied Science (IJRIAS)},
keywords = {ijrias, mathematics,},
month = {July},
number = 4,
pages = {01-09},
timestamp = {2016-10-13T10:22:21.000+0200},
title = {A Note on Adaptive Box-Cox Transformation Parameter in Linear Regression
},
url = {http://www.ijrias.org/DigitalLibrary/Vol.1&Issue4/01-09.pdf},
volume = 1,
year = 2016
}