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Arnone E, Ferraccioli F, Pigolotti C, Sangalli LM. A roughness penalty approach to estimate densities over two-dimensional manifolds. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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2
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Schneble M, Kauermann G. Intensity estimation on geometric networks with penalized splines. Ann Appl Stat 2022. [DOI: 10.1214/21-aoas1522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Marc Schneble
- Department of Statistics, Ludwig-Maximilians-Universität Munich
| | - Göran Kauermann
- Department of Statistics, Ludwig-Maximilians-Universität Munich
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D’Angelo N, Payares D, Adelfio G, Mateu J. Self-exciting point process modelling of crimes on linear networks. STAT MODEL 2022. [DOI: 10.1177/1471082x221094146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Although there are recent developments for the analysis of first and second-order characteristics of point processes on networks, there are very few attempts in introducing models for network data. Motivated by the analysis of crime data in Bucaramanga (Colombia), we propose a spatiotemporal Hawkes point process model adapted to events living on linear networks. We first consider a non-parametric modelling strategy, for which we follow a non-parametric estimation of both the background and the triggering components. Then we consider a semi-parametric version, including a parametric estimation of the background based on covariates, and a non-parametric one of the triggering effects. Our model can be easily adapted to multi-type processes. Our network model outperforms a planar version, improving the fitting of the self-exciting point process model.
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Affiliation(s)
- Nicoletta D’Angelo
- Department of Economics, Business and Statistics, University of Palermo, Sicily, Italy
| | - David Payares
- Department of Earth Observation Science, University of Twente, Overijssel, Netherlands
| | - Giada Adelfio
- Department of Economics, Business and Statistics, University of Palermo, Sicily, Italy
| | - Jorge Mateu
- Department of Mathematics, Universitat Jaume I, Valencian Community, Spain
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Moradi M, Mateu J, Comas C. Directional analysis for point patterns on linear networks. Stat (Int Stat Inst) 2021. [DOI: 10.1002/sta4.323] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Mehdi Moradi
- Department of Statistics, Computer Science, and Mathematics Public University of Navarre Pamplona 31006 Spain
- Institute of Advanced Materials and Mathematics (InaMat2) Public University of Navarre Pamplona 31006 Spain
| | - Jorge Mateu
- Department of Mathematics University Jaume I Castellón de la Plana 12071 Spain
| | - Carles Comas
- Department of Mathematics University of Lleida Lleida 25001 Spain
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Ferraccioli F, Arnone E, Finos L, Ramsay JO, Sangalli LM. Nonparametric density estimation over complicated domains. J R Stat Soc Series B Stat Methodol 2021. [DOI: 10.1111/rssb.12415] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Federico Ferraccioli
- Department of Statistical Sciences University of Padova Padova Veneto Italy
- MOX—Department of Mathematics Politecnico di Milano Milano Lombardia Italy
| | - Eleonora Arnone
- MOX—Department of Mathematics Politecnico di Milano Milano Lombardia Italy
| | - Livio Finos
- Department of Developmental Psychology and Socialisation University of Padova Padova Veneto Italy
| | | | - Laura M. Sangalli
- MOX—Department of Mathematics Politecnico di Milano Milano Lombardia Italy
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Chaudhuri S, Moradi M, Mateu J. On the trend detection of time-ordered intensity images of point processes on linear networks. COMMUN STAT-SIMUL C 2021. [DOI: 10.1080/03610918.2021.1881116] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Somnath Chaudhuri
- Institute of New Imaging Technologies (INIT), GEOTEC, University Jaume I, Castellón, Spain
| | - Mehdi Moradi
- Department of Statistics, Computer Science, and Mathematics, and Institute of Advanced Materials and Mathematics (InaMat2), Public University of Navarre, Pamplona, Spain
| | - Jorge Mateu
- Department of Mathematics, University Jaume I, Castellón, Spain
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7
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Moradi MM, Mateu J. First- and Second-Order Characteristics of Spatio-Temporal Point Processes on Linear Networks. J Comput Graph Stat 2019. [DOI: 10.1080/10618600.2019.1694524] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- M. Mehdi Moradi
- Institute of New Imaging Technologies, University Jaume I, Castellón, Spain
| | - Jorge Mateu
- Department of Mathematics, University Jaume I, Castellón, Spain
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