Please use this identifier to cite or link to this item:
Title: How to make plausibility-based forecasting more accurate
Authors: Kongliang Zhu
Nantiworn Thianpaen
Vladik Kreinovich
Keywords: Computer Science
Issue Date: 1-Feb-2017
Abstract: © Springer International Publishing AG 2017. In recent papers, a new plausibility-based forecasting method was proposed. While this method has been empirically successful, one of its steps—selecting a uniform probability distribution for the plausibility level—is heuristic. It is therefore desirable to check whether this selection is optimal or whether a modified selection would like to a more accurate forecast. In this paper, we show that the uniform distribution does not always lead to (asymptotically) optimal estimates, and we show how to modify the uniform-distribution step so that the resulting estimates become asymptotically optimal.
ISSN: 1860949X
Appears in Collections:CMUL: Journal Articles

Files in This Item:
There are no files associated with this item.

Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.