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In this paper we introduce randomized pivots for the means of short and longmemory linear processes. We show that, under the same conditions, these pivotsconverge in distribution to the same limit as that of their classicalnon-randomized counterparts. We also present numerical results that indicatethat these randomized pivots significantly outperform their classicalcounterparts and as a result they lead to a more accurate inference about thepopulation mean.

Randomized pivots for means of short and long memory linear processes