Rights statement: This is the peer reviewed version of the following article: Taylor, B. M., Andrade‐Pacheco, R. and Sturrock, H. J. (2018), Continuous inference for aggregated point process data. J. R. Stat. Soc. A, 181: 1125-1150. doi:10.1111/rssa.12347 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/rssa.12347/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
Accepted author manuscript, 26.2 MB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Continuous inference for aggregated point process data
AU - Taylor, B.M.
AU - Andrade-Pacheco, R.
AU - Sturrock, H.J.W.
N1 - This is the peer reviewed version of the following article: Taylor, B. M., Andrade‐Pacheco, R. and Sturrock, H. J. (2018), Continuous inference for aggregated point process data. J. R. Stat. Soc. A, 181: 1125-1150. doi:10.1111/rssa.12347 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/rssa.12347/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
PY - 2018/10
Y1 - 2018/10
N2 - The paper introduces new methods for inference with count data registered on a set of aggregation units. Such data are omnipresent in epidemiology because of confidentiality issues: it is much more common to know the county in which an individual resides, say, than to know their exact location in space. Inference for aggregated data has traditionally made use of models for discrete spatial variation, e.g. conditional auto-regressive models. We argue that such discrete models can be improved from both a scientific and an inferential perspective by using spatiotemporally continuous models to model the aggregated counts directly. We introduce methods for delivering (limiting) continuous inference with spatiotemporal aggregated count data in which the aggregation units might change over time and are subject to uncertainty. We illustrate our methods by using two examples: from epidemiology, spatial prediction of malaria incidence in Namibia, and, from politics, forecasting voting under the proposed changes to parliamentary boundaries in the UK. © 2018 Royal Statistical Society
AB - The paper introduces new methods for inference with count data registered on a set of aggregation units. Such data are omnipresent in epidemiology because of confidentiality issues: it is much more common to know the county in which an individual resides, say, than to know their exact location in space. Inference for aggregated data has traditionally made use of models for discrete spatial variation, e.g. conditional auto-regressive models. We argue that such discrete models can be improved from both a scientific and an inferential perspective by using spatiotemporally continuous models to model the aggregated counts directly. We introduce methods for delivering (limiting) continuous inference with spatiotemporal aggregated count data in which the aggregation units might change over time and are subject to uncertainty. We illustrate our methods by using two examples: from epidemiology, spatial prediction of malaria incidence in Namibia, and, from politics, forecasting voting under the proposed changes to parliamentary boundaries in the UK. © 2018 Royal Statistical Society
KW - Aggregation
KW - Change of support
KW - Downscaling
KW - Point processes
KW - Spatial misalignment
U2 - 10.1111/rssa.12347
DO - 10.1111/rssa.12347
M3 - Journal article
VL - 181
SP - 1125
EP - 1150
JO - Journal of the Royal Statistical Society: Series A Statistics in Society
JF - Journal of the Royal Statistical Society: Series A Statistics in Society
SN - 0964-1998
IS - 4
ER -