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Conditional intensity: a powerful tool for modelling and analysing point process data

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

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Conditional intensity: a powerful tool for modelling and analysing point process data. / Diggle, Peter.
In: Australian and New Zealand Journal of Statistics, Vol. 63, No. 1, 31.03.2021, p. 83-92.

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

Harvard

Diggle, P 2021, 'Conditional intensity: a powerful tool for modelling and analysing point process data', Australian and New Zealand Journal of Statistics, vol. 63, no. 1, pp. 83-92. https://doi.org/10.1111/anzs.12331

APA

Diggle, P. (2021). Conditional intensity: a powerful tool for modelling and analysing point process data. Australian and New Zealand Journal of Statistics, 63(1), 83-92. https://doi.org/10.1111/anzs.12331

Vancouver

Diggle P. Conditional intensity: a powerful tool for modelling and analysing point process data. Australian and New Zealand Journal of Statistics. 2021 Mar 31;63(1):83-92. doi: 10.1111/anzs.12331

Author

Diggle, Peter. / Conditional intensity : a powerful tool for modelling and analysing point process data. In: Australian and New Zealand Journal of Statistics. 2021 ; Vol. 63, No. 1. pp. 83-92.

Bibtex

@article{8c4fffc60d934b85aff201ca8ca3f9c3,
title = "Conditional intensity: a powerful tool for modelling and analysing point process data",
abstract = "The conditional intensity function of a spatial point process describes how the probability that a point of the process occurs at a particular point in its carrier space depends on the realisation of the process in the remainder of the carrier space. Provided that the point process is simple, the conditional intensity determines all of the properties of the process, in particular its likelihood function. In this paper we review the use of the conditional intensity function in the formulation of point process models and in making inferences from point process data, giving separate consideration to temporal, spatial and spatio-temporal settings. We argue that the conditional intensity function should take centre-stage in spatio-temporal point process modelling and analysis.",
keywords = "conditional intensity, point process, spatial, spatiotemporal",
author = "Peter Diggle",
year = "2021",
month = mar,
day = "31",
doi = "10.1111/anzs.12331",
language = "English",
volume = "63",
pages = "83--92",
journal = "Australian and New Zealand Journal of Statistics",
issn = "1369-1473",
publisher = "Wiley-Blackwell",
number = "1",

}

RIS

TY - JOUR

T1 - Conditional intensity

T2 - a powerful tool for modelling and analysing point process data

AU - Diggle, Peter

PY - 2021/3/31

Y1 - 2021/3/31

N2 - The conditional intensity function of a spatial point process describes how the probability that a point of the process occurs at a particular point in its carrier space depends on the realisation of the process in the remainder of the carrier space. Provided that the point process is simple, the conditional intensity determines all of the properties of the process, in particular its likelihood function. In this paper we review the use of the conditional intensity function in the formulation of point process models and in making inferences from point process data, giving separate consideration to temporal, spatial and spatio-temporal settings. We argue that the conditional intensity function should take centre-stage in spatio-temporal point process modelling and analysis.

AB - The conditional intensity function of a spatial point process describes how the probability that a point of the process occurs at a particular point in its carrier space depends on the realisation of the process in the remainder of the carrier space. Provided that the point process is simple, the conditional intensity determines all of the properties of the process, in particular its likelihood function. In this paper we review the use of the conditional intensity function in the formulation of point process models and in making inferences from point process data, giving separate consideration to temporal, spatial and spatio-temporal settings. We argue that the conditional intensity function should take centre-stage in spatio-temporal point process modelling and analysis.

KW - conditional intensity

KW - point process

KW - spatial

KW - spatiotemporal

U2 - 10.1111/anzs.12331

DO - 10.1111/anzs.12331

M3 - Journal article

VL - 63

SP - 83

EP - 92

JO - Australian and New Zealand Journal of Statistics

JF - Australian and New Zealand Journal of Statistics

SN - 1369-1473

IS - 1

ER -