文章基本信息

标题：Asymptotic Efficiency of the Maximum Likelihood Estimator for the Box-Cox Transformation Model with Heteroscedastic Disturbances
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作者：Kazumitsu Nawata
期刊名称：Open Journal of Statistics
印刷版ISSN：2161-718X
电子版ISSN：2161-7198
出版年度：2016
卷号：06
期号：05
页码：835-841
DOI：10.4236/ojs.2016.65069
语种：English
出版社：Scientific Research Publishing
摘要：This paper considers the asymptotic efficiency of the maximum likelihood estimator (MLE) for the Box-Cox transformation model with heteroscedastic disturbances. The MLE under the normality assumption (BC MLE) is a consistent and asymptotically efficient estimator if the “small ” condition is satisfied and the number of parameters is finite. However, the BC MLE cannot be asymptotically efficient and its rate of convergence is slower than ordinal order when the number of parameters goes to infinity. Anew consistent estimator of order is proposed. One important implication of this study is that estimation methods should be carefully chosen when the model contains many parameters in actual empirical studies.
关键词：Maximum Likelihood Estimator (MLE);Asymptotic Efficiency;Box-Cox Transformation Model;Heteroscedasticity