Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/105395
Title: Statistics with estimated parameters
Authors: Yang, Z.L.
Tse, Y.K.
Bai, Z.D. 
Keywords: Asymptotic distribution
Finite sample performance
Index parameter
Variance deflation
Variance inflation
Issue Date: Apr-2007
Citation: Yang, Z.L.,Tse, Y.K.,Bai, Z.D. (2007-04). Statistics with estimated parameters. Statistica Sinica 17 (2) : 817-837. ScholarBank@NUS Repository.
Abstract: This paper studies a general problem of making inferences for functions of two sets of parameters where, when the first set is given, there exists a statistic with a known distribution. We study the distribution of this statistic when the first set of parameters is unknown and is replaced by an estimator. We show that under mild conditions the variance of the statistic is inflated when the unconstrained maximum likelihood estimator (MLE) is used, but deflated when the constrained MLE is used. The results are shown to be useful in hypothesis testing and confidenceinterval construction in providing simpler and improved inference methods than do the standard large sample likelihood inference theories. We provide three applications of our theories, namely Box-Cox regression, dynamic regression, and spatial regression, to illustrate the generality and versatility of our results.
Source Title: Statistica Sinica
URI: http://scholarbank.nus.edu.sg/handle/10635/105395
ISSN: 10170405
Appears in Collections:Staff Publications

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