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Bivariate binomial spatial modelling of Loa loa prevalence in tropical Africa

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Bivariate binomial spatial modelling of Loa loa prevalence in tropical Africa. / Crainiceanu, C.; Diggle, Peter J.; Rowlingson, B. S.
In: Journal of the American Statistical Association, Vol. 103, No. 481, 2008, p. 21-37.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Crainiceanu, C, Diggle, PJ & Rowlingson, BS 2008, 'Bivariate binomial spatial modelling of Loa loa prevalence in tropical Africa', Journal of the American Statistical Association, vol. 103, no. 481, pp. 21-37. https://doi.org/10.1198/016214507000001409

APA

Crainiceanu, C., Diggle, P. J., & Rowlingson, B. S. (2008). Bivariate binomial spatial modelling of Loa loa prevalence in tropical Africa. Journal of the American Statistical Association, 103(481), 21-37. https://doi.org/10.1198/016214507000001409

Vancouver

Crainiceanu C, Diggle PJ, Rowlingson BS. Bivariate binomial spatial modelling of Loa loa prevalence in tropical Africa. Journal of the American Statistical Association. 2008;103(481):21-37. doi: 10.1198/016214507000001409

Author

Crainiceanu, C. ; Diggle, Peter J. ; Rowlingson, B. S. / Bivariate binomial spatial modelling of Loa loa prevalence in tropical Africa. In: Journal of the American Statistical Association. 2008 ; Vol. 103, No. 481. pp. 21-37.

Bibtex

@article{93b198263c534fd8b6848923074dd749,
title = "Bivariate binomial spatial modelling of Loa loa prevalence in tropical Africa",
abstract = "We present a state-of-the-art application of smoothing for dependent bivariate binomial spatial data to Loa loa prevalence mapping in West Africa. This application starts with the nonspatial calibration of survey instruments, continues with the spatial model building and assessment, and ends with robust, tested software intended for use by field workers for online prevalence map updating. From a statistical perspective, we address several important methodological issues: building spatial models that are sufficiently complex to capture the structure of the data but remain computationally usable, reducing the computational burden in the handling of very large covariate data sets, and devising methods for comparing spatial prediction methods for a given exceedance policy threshold.",
keywords = "Geostatistics, Low rank, Thin-plate splines",
author = "C. Crainiceanu and Diggle, {Peter J.} and Rowlingson, {B. S.}",
year = "2008",
doi = "10.1198/016214507000001409",
language = "English",
volume = "103",
pages = "21--37",
journal = "Journal of the American Statistical Association",
issn = "1537-274X",
publisher = "Taylor and Francis Ltd.",
number = "481",

}

RIS

TY - JOUR

T1 - Bivariate binomial spatial modelling of Loa loa prevalence in tropical Africa

AU - Crainiceanu, C.

AU - Diggle, Peter J.

AU - Rowlingson, B. S.

PY - 2008

Y1 - 2008

N2 - We present a state-of-the-art application of smoothing for dependent bivariate binomial spatial data to Loa loa prevalence mapping in West Africa. This application starts with the nonspatial calibration of survey instruments, continues with the spatial model building and assessment, and ends with robust, tested software intended for use by field workers for online prevalence map updating. From a statistical perspective, we address several important methodological issues: building spatial models that are sufficiently complex to capture the structure of the data but remain computationally usable, reducing the computational burden in the handling of very large covariate data sets, and devising methods for comparing spatial prediction methods for a given exceedance policy threshold.

AB - We present a state-of-the-art application of smoothing for dependent bivariate binomial spatial data to Loa loa prevalence mapping in West Africa. This application starts with the nonspatial calibration of survey instruments, continues with the spatial model building and assessment, and ends with robust, tested software intended for use by field workers for online prevalence map updating. From a statistical perspective, we address several important methodological issues: building spatial models that are sufficiently complex to capture the structure of the data but remain computationally usable, reducing the computational burden in the handling of very large covariate data sets, and devising methods for comparing spatial prediction methods for a given exceedance policy threshold.

KW - Geostatistics

KW - Low rank

KW - Thin-plate splines

UR - http://www.scopus.com/inward/record.url?scp=42349084895&partnerID=8YFLogxK

U2 - 10.1198/016214507000001409

DO - 10.1198/016214507000001409

M3 - Journal article

VL - 103

SP - 21

EP - 37

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

SN - 1537-274X

IS - 481

ER -