title: Estimation of transformation parameters for microarray data.
abstract: RESULTS
  Durbin et al. (2002), Huber et al. (2002) and Munson (2001) independently introduced a family of transformations (the generalized-log family) which stabilizes the variance of microarray data up to the first order. We introduce a method for estimating the transformation parameter in tandem with a linear model based on the procedure outlined in Box and Cox (1964). We also discuss means of finding transformations within the generalized-log family which are optimal under other criteria, such as minimum residual skewness and minimum mean-variance dependency.
  
  
  BACKGROUND
  R and Matlab code and test data are available from the authors on request.
authors.affiliation: Department of Statistics, UC Davis, Davis, CA 95616, USA. bpdurbin@ucdavis.edu
authors.forename: Blythe
authors.initials: B
authors.lastname: Durbin
authors.forename: David M
authors.initials: DM
authors.lastname: Rocke
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D007938:Leukemia / Q000235:genetics; D016013:Likelihood Functions; D016014:Linear Models; D008297:Male; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D051381:Rats; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity