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Box‐Cox transformations in linear models: Large sample theory and tests of normality
Authors:Gemai Chen  Richard A Lockhart  Michael A Stephens
Abstract:The authors provide a rigorous large sample theory for linear models whose response variable has been subjected to the Box‐Cox transformation. They provide a continuous asymptotic approximation to the distribution of estimators of natural parameters of the model. They show, in particular, that the maximum likelihood estimator of the ratio of slope to residual standard deviation is consistent and relatively stable. The authors further show the importance for inference of normality of the errors and give tests for normality based on the estimated residuals. For non‐normal errors, they give adjustments to the log‐likelihood and to asymptotic standard errors.
Keywords:Empirical distribution function  goodness of fit  linear regression  maximum likelihood estimation  non‐linear regression  transformations to normality
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