A non-parametric analysis of transformations
A non-parametric estimator is proposed for the transformation model, where, following \textit{G. E. P. Box} and \textit{D. R. Cases tables are also provided for specific values of \(\epsilon\), and the joint confidence coefficient 1-\(\alpha\). For the general case, a method to compute the sample size is developed resulting in an integral equation involving the covariance matrix. In case a prior estimate of the covariance matrix is available, the integral equation can be solved by using the algorithm given by \textit{N. S. Russell}}, \textit{D. R. Farrier} and \textit{J. Howell}, J. R. Stat. Soc., Ser. C 34, 49-53 (1985; Zbl 0571.60023). Examples are used to illustrate the effects of dimensions and quality of prior estimates of covariance matrices on the sample size.
scientific article; zbMATH DE number 4060536
scientific article