Inference in successive sampling discovery models

Journal Article (Journal Article)

Successive sampling discovery problems arise in finite population sampling subject to 'size-biased' selection mechanisms. Formal statistical analysis of discovery data under such models is technically challenging. Bayesian analyses are developed here in a superpopulation framework. We show how simulation methods provide computation of posterior distributions for superpopulation parameters and, more critically, predictive inferences for unsampled units in the finite population. Model extensions cover problems of uncertainty about finite population sizes, uncertainty about sample selection mechanisms, and other practical issues. Several analyses of published oil reserve data are used for illustration.

Full Text

Duke Authors

Cited Authors

  • West, M

Published Date

  • January 1, 1996

Published In

Volume / Issue

  • 75 / 1

Start / End Page

  • 217 - 238

International Standard Serial Number (ISSN)

  • 0304-4076

Digital Object Identifier (DOI)

  • 10.1016/0304-4076(95)01777-1

Citation Source

  • Scopus