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On preliminary test and shrinkage estimation in linear models with long memory errors

Research output: Contribution to journalJournal article


<mark>Journal publication date</mark>15/06/1998
<mark>Journal</mark>Journal of Statistical Planning and Inference
Issue number2
Number of pages10
Pages (from-to)319-328
<mark>Original language</mark>English


This paper discusses the problem of estimating a subset of parameters when the complementary subset is possibly redundant, in a linear regression model when the errors are generated from a long-memory process. Such a model arises due to the overmodelling of a situation involving long-memory data. Along with the classical least-squares estimator and restricted least-squares estimator, preliminary test least-squares estimator and shrinkage least-squares estimator are investigated in an asymptotic set-up and their relative performances are studied under contiguous alternatives. The contiguous alternatives under such dependence are fundamentally different from those under the independent errors case.