Characterising Uncertainty in Expert Assessments: Encoding Heavily Skewed Judgements.

ABSTRACT: When limited or no observed data are available, it is often useful to obtain expert knowledge about parameters of interest, including point estimates and the uncertainty around these values. However, it is vital to elicit this information appropriately in order to obtain valid estimates. This is particularly important when the experts' uncertainty about these estimates is strongly skewed, for instance when their best estimate is the same as the lowest value they consider possible. Also this is important when interest is in the aggregation of elicited values. In this paper, we compare alternative distributions for describing such estimates. The distributions considered include the lognormal, mirror lognormal, Normal and scaled Beta. The case study presented here involves estimation of the n

SUBMITTER: O'Leary RA 

PROVIDER: S-EPMC4627781 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature