dc.date.accessioned | 2021-10-18T12:39:07Z | |
dc.date.available | 2021-10-18T12:39:07Z | |
dc.date.issued | 2021-02-21 | |
dc.identifier.citation | Sengupta, D., Banerjee, T., & Roy, S. (2020). Estimation of Poisson mean with under‐reported counts: a double sampling approach. Australian & New Zealand Journal of Statistics, 62(4), 508-535. | en_US |
dc.identifier.uri | https://doi.org/10.1111/anzs.12308 | |
dc.identifier.uri | http://hdl.handle.net/11718/24414 | |
dc.description.abstract | Count data arising in various fields of applications are often under-reported. Ignoring undercount naturally leads to biased estimators and inaccurate confidence intervals. In the presence of undercount, in this paper, we develop likelihood-based methodologies for estimation of mean using validation data. The asymptotic distributions of the competing estimators of the mean are derived. The impact of ignoring undercount on the coverage and length of the confidence intervals is investigated using extensive numerical studies. Finally an analysis of heat mortality data is presented. | en_US |
dc.language.iso | en | en_US |
dc.publisher | John Wiley & Sons, Inc. | en_US |
dc.relation.ispartof | Australian and New Zealand Journal of Statistics | en_US |
dc.subject | Confidence intervals | en_US |
dc.subject | Maximum likelihood | en_US |
dc.subject | Pseudo-likelihood | en_US |
dc.title | Estimation of Poisson mean with under-reported counts: a double sampling approach | en_US |
dc.type | Article | en_US |