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Journal articles on the topic 'Marked point processes'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Vere-Jones, David, and Frederic Paik Schoenberg. "Rescaling Marked Point Processes." Australian New Zealand Journal of Statistics 46, no. 1 (March 2004): 133–43. http://dx.doi.org/10.1111/j.1467-842x.2004.00319.x.

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2

McElroy, Tucker, and Dimitris N. Politis. "Stable marked point processes." Annals of Statistics 35, no. 1 (February 2007): 393–419. http://dx.doi.org/10.1214/009053606000001163.

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3

Xu, C., P. A. Dowd, K. V. Mardia, R. J. Fowell, and C. C. Taylor. "Simulating Correlated Marked-point Processes." Journal of Applied Statistics 34, no. 9 (November 2007): 1125–34. http://dx.doi.org/10.1080/02664760701597231.

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4

Renshaw, Eric, Jorge Mateu, and Fuensanta Saura. "Disentangling mark/point interaction in marked-point processes." Computational Statistics & Data Analysis 51, no. 6 (March 2007): 3123–44. http://dx.doi.org/10.1016/j.csda.2006.07.035.

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5

Pawlas, Zbyněk. "Empirical distributions in marked point processes." Stochastic Processes and their Applications 119, no. 12 (December 2009): 4194–209. http://dx.doi.org/10.1016/j.spa.2009.10.002.

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6

Dimitrov, Boyan, Stefanka Chukova, and Ventseslav Rumchev. "Warranty claims as marked point processes." Nonlinear Analysis: Theory, Methods & Applications 47, no. 3 (August 2001): 2145–50. http://dx.doi.org/10.1016/s0362-546x(01)00340-6.

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7

Dshalalow, Jewgeni H. "On multivariate antagonistic marked point processes." Mathematical and Computer Modelling 49, no. 3-4 (February 2009): 432–52. http://dx.doi.org/10.1016/j.mcm.2008.07.029.

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8

Sigman, Karl, and Ward Whitt. "Marked point processes in discrete time." Queueing Systems 92, no. 1-2 (April 16, 2019): 47–81. http://dx.doi.org/10.1007/s11134-019-09612-3.

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9

Li, Chen, and Yuping Song. "Exponential Inequality of Marked Point Processes." Mathematics 11, no. 4 (February 9, 2023): 881. http://dx.doi.org/10.3390/math11040881.

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This paper presents the uniform concentration inequality for the stochastic integral of marked point processes. We developed a new chaining method to obtain the results. Our main result is presented under an entropy condition for partitioning the index set of the integrands. Our result is an improvement of the work of van de Geer on exponential inequalities for martingales in 1995. As applications of the main result, we also obtained the uniform concentration inequality of functional indexed empirical processes and the Kakutani–Hellinger distance of the maximum likelihood estimator.

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10

Schoenberg, Frederic Paik. "On Non-simple Marked Point Processes." Annals of the Institute of Statistical Mathematics 58, no. 2 (March 4, 2006): 223–33. http://dx.doi.org/10.1007/s10463-005-0003-y.

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11

van Lieshout, M. N. M., and R. S. Stoica. "Perfect simulation for marked point processes." Computational Statistics & Data Analysis 51, no. 2 (November 2006): 679–98. http://dx.doi.org/10.1016/j.csda.2006.02.023.

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12

Penttinen, Antti, Dietrich Stoyan, and Helena M. Henttonen. "Marked Point Processes in Forest Statistics." Forest Science 38, no. 4 (November 1, 1992): 806–24. http://dx.doi.org/10.1093/forestscience/38.4.806.

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Abstract Trees in a forest interact and therefore have to be considered as a system of dependent random variates from an unknown stochastic process. One such mathematical model which considers the spatial dependence among trees in a forest and their characteristics is a marked point process. The "points" are the tree positions and the "marks" are tree characteristics such as stem diameters or tree species. Statistical methods for marked point processes can give valuable information on the spatial interaction of trees. They are applied in this paper to describe spatial dependence of stem diameters in a spruce forest, of heights of trees in a stand of pine saplings, and of heights, stem diameters, and crown lengths in a mixed birch-pine forest area. For. Sci. 38(4):806-824.

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13

Ho, Lai Ping, and D. Stoyan. "Modelling marked point patterns by intensity-marked Cox processes." Statistics & Probability Letters 78, no. 10 (August 2008): 1194–99. http://dx.doi.org/10.1016/j.spl.2007.11.013.

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14

Bjork, Tomas, Yuri Kabanov, and Wolfgang Runggaldier. "Bond Market Structure in the Presence of Marked Point Processes." Mathematical Finance 7, no. 2 (April 1997): 211–39. http://dx.doi.org/10.1111/1467-9965.00031.

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15

Kofman, D., and H. Korezlioglu. "Some EATA properties for marked point processes." Journal of Applied Probability 32, no. 4 (December 1995): 922–29. http://dx.doi.org/10.2307/3215205.

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We derive an ESTA property for marked point processes similar to Wolff's PASTA property for ordinary (non-marked) point processes, via a stochastic integral approach. This new ESTA property allows us to extend a known result on the conditional PASTA property and to derive an ASTA property for batch arrival processes. We also present an application of our results.

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16

Kofman, D., and H. Korezlioglu. "Some EATA properties for marked point processes." Journal of Applied Probability 32, no. 04 (December 1995): 922–29. http://dx.doi.org/10.1017/s0021900200103390.

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We derive an ESTA property for marked point processes similar to Wolff's PASTA property for ordinary (non-marked) point processes, via a stochastic integral approach. This new ESTA property allows us to extend a known result on the conditional PASTA property and to derive an ASTA property for batch arrival processes. We also present an application of our results.

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17

Elliott, Robert J., Allanus H. Tsoi, and Shiu Hong Lui. "Short rate analysis and marked point processes." Mathematical Methods of Operations Research (ZOR) 50, no. 1 (August 17, 1999): 149–60. http://dx.doi.org/10.1007/s001860050041.

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18

Cronie, O., and M. N. M. van Lieshout. "Summary statistics for inhomogeneous marked point processes." Annals of the Institute of Statistical Mathematics 68, no. 4 (March 14, 2015): 905–28. http://dx.doi.org/10.1007/s10463-015-0515-z.

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19

Diao, Liqun, Richard J. Cook, and Ker-Ai Lee. "A copula model for marked point processes." Lifetime Data Analysis 19, no. 4 (May 10, 2013): 463–89. http://dx.doi.org/10.1007/s10985-013-9259-3.

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20

Morariu-Patrichi, Maxime, and Mikko S. Pakkanen. "Hybrid Marked Point Processes: Characterization, Existence and Uniqueness." Market Microstructure and Liquidity 04, no. 03n04 (September 2018): 1950007. http://dx.doi.org/10.1142/s2382626619500072.

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In this paper, we introduce a class of hybrid marked point processes, which encompasses and extends continuous-time Markov chains and Hawkes processes. While this flexible class amalgamates such existing processes, it also contains novel processes with complex dynamics. These processes are defined implicitly via their intensity and are endowed with a state process that interacts with past-dependent events. The key example we entertain is an extension of a Hawkes process, a state-dependent Hawkes process interacting with its state process. We show the existence and uniqueness of hybrid marked point processes under general assumptions, extending the results of Massoulié (1998) on interacting point processes.

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21

Dong, Gang. "Detection of rolling leukocytes by marked point processes." Journal of Electronic Imaging 16, no. 3 (July 1, 2007): 033013. http://dx.doi.org/10.1117/1.2774829.

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22

Politis, Dimitris N., and Michael Sherman. "Moment estimation for statistics from marked point processes." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63, no. 2 (May 2001): 261–75. http://dx.doi.org/10.1111/1467-9868.00284.

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23

Paik Schoenberg, Frederic. "Testing Separability in Spatial-Temporal Marked Point Processes." Biometrics 60, no. 2 (June 2004): 471–81. http://dx.doi.org/10.1111/j.0006-341x.2004.00192.x.

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24

Tempel, Elmo, Maarja Kruuse, Rain Kipper, Taavi Tuvikene, Jenny G. Sorce, and Radu S. Stoica. "Bayesian group finder based on marked point processes." Astronomy & Astrophysics 618 (October 2018): A81. http://dx.doi.org/10.1051/0004-6361/201833217.

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Context. Galaxy groups and clusters are formidable cosmological probes. They permit the studying of the environmental effects on galaxy formation. A reliable detection of galaxy groups is an open problem and is important for ongoing and future cosmological surveys. Aims. We propose a probabilistic galaxy group detection algorithm based on marked point processes with interactions. Methods. The pattern of galaxy groups in a catalogue is seen as a random set of interacting objects. The positions and the interactions of these objects are governed by a probability density. The parameters of the probability density were chosen using a priori knowledge. The estimator of the unknown cluster pattern is given by the configuration of objects maximising the proposed probability density. Adopting the Bayesian framework, the proposed probability density is maximised using a simulated annealing (SA) algorithm. At fixed temperature, the SA algorithm is a Monte Carlo sampler of the probability density. Hence, the method provides “for free” additional information such as the probabilities that a point or two points in the observation domain belong to the cluster pattern, respectively. These supplementary tools allow the construction of tests and techniques to validate and to refine the detection result. Results. To test the feasibility of the proposed methodology, we applied it to the well-studied 2MRS data set. Compared to previously published Friends-of-Friends (FoF) group finders, the proposed Bayesian group finder gives overall similar results. However for specific applications, like the reconstruction of the local Universe, the details of the grouping algorithms are important. Conclusions. The proposed Bayesian group finder is tested on a galaxy redshift survey, but more detailed analyses are needed to understand the actual capabilities of the algorithm regarding upcoming cosmological surveys. The presented mathematical framework permits adapting it easily for other data sets (in astronomy and in other fields of sciences). In cosmology, one promising application is the detection of galaxy groups in photometric galaxy redshift surveys, while taking into account the full photometric redshift posteriors.

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25

Alonso-Ruiz, P., and E. Spodarev. "Estimation of entropy for Poisson marked point processes." Advances in Applied Probability 49, no. 1 (March 2017): 258–78. http://dx.doi.org/10.1017/apr.2016.87.

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Abstract In this paper a kernel estimator of the differential entropy of the mark distribution of a homogeneous Poisson marked point process is proposed. The marks have an absolutely continuous distribution on a compact Riemannian manifold without boundary. We investigate L2 and the almost surely consistency of this estimator as well as its asymptotic normality.

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26

Karyagina, Marina, Walter Wong, and Ljubica Vlacic. "Life cycle cost modelling using marked point processes." Reliability Engineering & System Safety 59, no. 3 (March 1998): 291–98. http://dx.doi.org/10.1016/s0951-8320(97)00086-0.

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27

Barna, Keresztes, Tamás Szirányi, Monica Borda, and Olivier Lavialle. "Marked point processes for enhancing seismic fault patterns." Journal of Applied Geophysics 118 (July 2015): 115–23. http://dx.doi.org/10.1016/j.jappgeo.2015.04.009.

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28

Touzi, Nizar. "Optimal insurance demand under marked point processes shocks." Annals of Applied Probability 10, no. 1 (February 2000): 283–312. http://dx.doi.org/10.1214/aoap/1019737674.

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29

Renshaw, Eric. "Two-Dimensional Spectral Analysis for Marked Point Processes." Biometrical Journal 44, no. 6 (September 2002): 718–45. http://dx.doi.org/10.1002/1521-4036(200209)44:6<718::aid-bimj718>3.0.co;2-6.

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30

Madrid, A. E., J. M. Angulo, and J. Mateu. "Spatial threshold exceedance analysis through marked point processes." Environmetrics 23, no. 1 (December 19, 2011): 108–18. http://dx.doi.org/10.1002/env.1141.

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31

Schlather, Martin. "Point Process Theory and Applications: Marked Point and Piecewise Deterministic Processes." Journal of the American Statistical Association 102, no. 479 (September 2007): 1077–78. http://dx.doi.org/10.1198/jasa.2007.s206.

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32

Gu, Yulong. "Attentive Neural Point Processes for Event Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 7592–600. http://dx.doi.org/10.1609/aaai.v35i9.16929.

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Event sequence, where each event is associated with a marker and a timestamp, is increasingly ubiquitous in various applications. Accordingly, event forecasting emerges to be a crucial problem, which aims to predict the next event based on the historical sequence. In this paper, we propose ANPP, an Attentive Neural Point Processes framework to solve this problem. In comparison with state-of-the-art methods like recurrent marked temporal point processes, ANPP leverages the time-aware self-attention mechanism to explicitly model the influence between every pair of historical events, resulting in more accurate predictions of events and better interpretation ability. Extensive experiments on one synthetic and four real-world datasets demonstrate that ANPP can achieve significant performance gains against state-of-the-art methods for predictions of both timings and markers. To facilitate future research, we release the codes and datasets at https://github.com/guyulongcs/AAAI2021\_ANPP.

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33

Schmidt, A., C. Kruse, F. Rottensteiner, U. Soergel, and C. Heipke. "NETWORK DETECTION IN RASTER DATA USING MARKED POINT PROCESSES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 701–8. http://dx.doi.org/10.5194/isprs-archives-xli-b3-701-2016.

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We propose a new approach for the automatic detection of network structures in raster data. The model for the network structure is represented by a graph whose nodes and edges correspond to junction-points and to connecting line segments, respectively; nodes and edges are further described by certain parameters. We embed this model in the probabilistic framework of marked point processes and determine the most probable configuration of objects by stochastic sampling. That is, different graph configurations are constructed randomly by modifying the graph entity parameters, by adding and removing nodes and edges to/ from the current graph configuration. Each configuration is then evaluated based on the probabilities of the changes and an energy function describing the conformity with a predefined model. By using the Reversible Jump Markov Chain Monte Carlo sampler, a global optimum of the energy function is determined. We apply our method to the detection of river and tidal channel networks in digital terrain models. In comparison to our previous work, we introduce constraints concerning the flow direction of water into the energy function. Our goal is to analyse the influence of different parameter settings on the results of network detection in both, synthetic and real data. Our results show the general potential of our method for the detection of river networks in different types of terrain.

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34

Asmussen, Søren, and Ger Koole. "Marked point processes as limits of Markovian arrival streams." Journal of Applied Probability 30, no. 2 (June 1993): 365–72. http://dx.doi.org/10.2307/3214845.

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A Markovian arrival stream is a marked point process generated by the state transitions of a given Markovian environmental process and Poisson arrival rates depending on the environment. It is shown that to a given marked point process there is a sequence of such Markovian arrival streams with the property that as m →∞. Various related corollaries (involving stationarity, convergence of moments and ergodicity) and counterexamples are discussed as well.

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35

Schlather, Martin. "On the Second-Order Characteristics of Marked Point Processes." Bernoulli 7, no. 1 (February 2001): 99. http://dx.doi.org/10.2307/3318604.

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36

Garner, William, and Dimitris N. Politis. "Local block bootstrap for inhomogeneous Poisson marked point processes." Bernoulli 24, no. 1 (February 2018): 592–615. http://dx.doi.org/10.3150/16-bej889.

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37

Schmidt, A., C. Kruse, F. Rottensteiner, U. Soergel, and C. Heipke. "NETWORK DETECTION IN RASTER DATA USING MARKED POINT PROCESSES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 701–8. http://dx.doi.org/10.5194/isprsarchives-xli-b3-701-2016.

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Abstract:

We propose a new approach for the automatic detection of network structures in raster data. The model for the network structure is represented by a graph whose nodes and edges correspond to junction-points and to connecting line segments, respectively; nodes and edges are further described by certain parameters. We embed this model in the probabilistic framework of marked point processes and determine the most probable configuration of objects by stochastic sampling. That is, different graph configurations are constructed randomly by modifying the graph entity parameters, by adding and removing nodes and edges to/ from the current graph configuration. Each configuration is then evaluated based on the probabilities of the changes and an energy function describing the conformity with a predefined model. By using the Reversible Jump Markov Chain Monte Carlo sampler, a global optimum of the energy function is determined. We apply our method to the detection of river and tidal channel networks in digital terrain models. In comparison to our previous work, we introduce constraints concerning the flow direction of water into the energy function. Our goal is to analyse the influence of different parameter settings on the results of network detection in both, synthetic and real data. Our results show the general potential of our method for the detection of river networks in different types of terrain.

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38

Sakaguchi, Takayuki, and Shigeru Mase. "On the Threshold Method for Marked Spatial Point Processes." JOURNAL OF THE JAPAN STATISTICAL SOCIETY 33, no. 1 (2003): 23–37. http://dx.doi.org/10.14490/jjss.33.23.

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39

Abdurachman, Edi, and H. T. David. "Cesaro limits of marked point processes oh the line." Communications in Statistics. Stochastic Models 4, no. 1 (January 1988): 77–98. http://dx.doi.org/10.1080/15326348808807071.

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40

Syversveen, Anne Randi, and Henning Omre. "Conditioning of Marked Point Processes within a Bayesian Framework." Scandinavian Journal of Statistics 24, no. 3 (September 1997): 341–52. http://dx.doi.org/10.1111/1467-9469.00068.

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41

Asmussen, Søren, and Ger Koole. "Marked point processes as limits of Markovian arrival streams." Journal of Applied Probability 30, no. 02 (June 1993): 365–72. http://dx.doi.org/10.1017/s0021900200117371.

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Abstract:

A Markovian arrival stream is a marked point process generated by the state transitions of a given Markovian environmental process and Poisson arrival rates depending on the environment. It is shown that to a given marked point process there is a sequence of such Markovian arrival streams with the property that as m →∞. Various related corollaries (involving stationarity, convergence of moments and ergodicity) and counterexamples are discussed as well.

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42

Shibue, Ryohei, and Fumiyasu Komaki. "Deconvolution of calcium imaging data using marked point processes." PLOS Computational Biology 16, no. 3 (March 12, 2020): e1007650. http://dx.doi.org/10.1371/journal.pcbi.1007650.

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43

COEURJOLLY, JEAN-FRANOIS, DAVID DEREUDRE, RÉMY DROUILHET, and FRÉDÉRIC LAVANCIER. "Takacs-Fiksel Method for Stationary Marked Gibbs Point Processes." Scandinavian Journal of Statistics 39, no. 3 (July 12, 2011): 416–43. http://dx.doi.org/10.1111/j.1467-9469.2011.00738.x.

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44

Jevtić, Petar, Marina Marena, and Patrizia Semeraro. "A note on Marked Point Processes and multivariate subordination." Statistics & Probability Letters 122 (March 2017): 162–67. http://dx.doi.org/10.1016/j.spl.2016.11.008.

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45

Elmossaoui, Hichem, Nadia Oukid, and Farouk Hannane. "Construction of computer experiment designs using marked point processes." Afrika Matematika 31, no. 5-6 (February 4, 2020): 917–28. http://dx.doi.org/10.1007/s13370-020-00770-9.

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46

Ge, Weina, and Robert Collins. "Crowd Density Analysis with Marked Point Processes [Applications Corner." IEEE Signal Processing Magazine 27, no. 5 (September 2010): 107–23. http://dx.doi.org/10.1109/msp.2010.937495.

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47

Chang, Chien-Hsun, and Frederic Paik Schoenberg. "Testing separability in marked multidimensional point processes with covariates." Annals of the Institute of Statistical Mathematics 63, no. 6 (June 2, 2010): 1103–22. http://dx.doi.org/10.1007/s10463-010-0284-7.

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48

Malinowski, Alexander, Martin Schlather, and Zhengjun Zhang. "Intrinsically weighted means and non-ergodic marked point processes." Annals of the Institute of Statistical Mathematics 68, no. 1 (September 21, 2014): 1–24. http://dx.doi.org/10.1007/s10463-014-0485-6.

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49

Zhang, Tonglin, and Qianlai Zhuang. "On the local odds ratio between points and marks in marked point processes." Spatial Statistics 9 (August 2014): 20–37. http://dx.doi.org/10.1016/j.spasta.2013.12.002.

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50

Semeĭko, M. G. "Moment measures of mixed empirical random point processes and marked point processes in compact metric spaces. I." Theory of Probability and Mathematical Statistics 88 (July 24, 2014): 161–74. http://dx.doi.org/10.1090/s0094-9000-2014-00926-6.

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