Journal articles: 'Temporal statistics'

1

Bussberg, Nicholas W. "Spatio-Temporal Statistics With R." American Statistician 75, no. 1 (January 2, 2021): 114. http://dx.doi.org/10.1080/00031305.2020.1865066.

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Subba Rao, T. "Statistics for Spatio-Temporal Data." Journal of Time Series Analysis 33, no. 4 (May 22, 2012): 699–700. http://dx.doi.org/10.1111/j.1467-9892.2011.00765.x.

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Robert, Christian. "Statistics for Spatio-Temporal Data." CHANCE 27, no. 2 (April 3, 2014): 64. http://dx.doi.org/10.1080/09332480.2014.914769.

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Naus, Joseph, and Sylvan Wallenstein. "Temporal surveillance using scan statistics." Statistics in Medicine 25, no. 2 (2005): 311–24. http://dx.doi.org/10.1002/sim.2209.

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Subba Rao, T. "Spatial statistics and spatio-temporal data." Journal of Time Series Analysis 34, no. 2 (October 24, 2012): 280. http://dx.doi.org/10.1111/j.1467-9892.2012.00821.x.

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6

Tang, X. Z., E. R. Tracy, and R. Brown. "Symbol statistics and spatio-temporal systems." Physica D: Nonlinear Phenomena 102, no. 3-4 (April 1997): 253–61. http://dx.doi.org/10.1016/s0167-2789(96)00201-1.

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Lim, Chia Wei, and Michael B. Wakin. "Compressive Temporal Higher Order Cyclostationary Statistics." IEEE Transactions on Signal Processing 63, no. 11 (June 2015): 2942–56. http://dx.doi.org/10.1109/tsp.2015.2415760.

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Dettmann, Carl P., and Orestis Georgiou. "Isolation statistics in temporal spatial networks." EPL (Europhysics Letters) 119, no. 2 (July 1, 2017): 28002. http://dx.doi.org/10.1209/0295-5075/119/28002.

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Nguyen, Benjamin, and Michael J. Spivey. "Journal of Multiscale Neuroscience." Journal of Multiscale Neuroscience 2, no. 1 (April 28, 2023): 251–72. http://dx.doi.org/10.56280/1570699699.

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By juxtaposing time series analyses of activity measured from a fully recurrent network undergoing disrupted processing and of activity measured from a continuous meta-cognitive report of disruption in real-time language comprehension, we present an opportunity to compare the temporal statistics of the state-space trajectories inherent to both systems. Both the recurrent network and the human language comprehension process appear to exhibit long-range temporal correlations and low entropy when processing is undisrupted and coordinated. However, when processing is disrupted and discoordinated, they both exhibit more short-range temporal correlations and higher entropy. We conclude that by measuring human language comprehension in a dense-sampling manner similar to how we analyze the networks, and analyzing the resulting data stream with nonlinear time series analysis techniques, we can obtain more insight into the temporal character of these discoordination phases than by simply marking the points in time at which they peak.

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Nguyen, Benjamin, and Michael J. Spivey. "Journal of Multiscale Neuroscience." Journal of Multiscale Neuroscience 2, no. 1 (April 28, 2023): 251–72. http://dx.doi.org/10.56280/1570857416.

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By juxtaposing time series analyses of activity measured from a fully recurrent network undergoing disrupted processing and of activity measured from a continuous meta-cognitive report of disruption in real-time language comprehension, we present an opportunity to compare the temporal statistics of the state-space trajectories inherent to both systems. Both the recurrent network and the human language comprehension process appear to exhibit long-range temporal correlations and low entropy when processing is undisrupted and coordinated. However, when processing is disrupted and discoordinated, they both exhibit more short-range temporal correlations and higher entropy. We conclude that by measuring human language comprehension in a dense-sampling manner similar to how we analyze the networks, and analyzing the resulting data stream with nonlinear time series analysis techniques, we can obtain more insight into the temporal character of these discoordination phases than by simply marking the points in time at which they peak.

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Harrington Jr., John A., and Randall S. Cerveny. "TEMPORAL STATISTICS: AN APPLICATION IN SNOWFALL CLIMATOLOGY." Physical Geography 9, no. 4 (October 1988): 337–53. http://dx.doi.org/10.1080/02723646.1988.10642358.

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12

González, Jonatan A., Francisco J. Rodríguez-Cortés, Ottmar Cronie, and Jorge Mateu. "Spatio-temporal point process statistics: A review." Spatial Statistics 18 (November 2016): 505–44. http://dx.doi.org/10.1016/j.spasta.2016.10.002.

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13

Paulson, Kevin S. "Spatial-temporal statistics of rainrate random fields." Radio Science 37, no. 5 (October 2002): 21–1. http://dx.doi.org/10.1029/2001rs002527.

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14

Link, Bennett, and Richard I. Epstein. "Statistics of Gamma-Ray Burst Temporal Asymmetry." Astrophysical Journal 466 (August 1996): 764. http://dx.doi.org/10.1086/177550.

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15

Janeja, Vandana P., Aryya Gangopadhyay, Seyed Mohammadi, and Revathi Palanisamy. "Discovery of anomalous spatio-temporal windows using discretized spatio-temporal scan statistics." Statistical Analysis and Data Mining 4, no. 3 (April 18, 2011): 276–300. http://dx.doi.org/10.1002/sam.10122.

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16

Pless, R., T. Brodsky, and Y. Aloimonos. "Detecting independent motion: the statistics of temporal continuity." IEEE Transactions on Pattern Analysis and Machine Intelligence 22, no. 8 (2000): 768–73. http://dx.doi.org/10.1109/34.868679.

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17

Laurson, Lasse, Xavier Illa, and Mikko J. Alava. "The effect of thresholding on temporal avalanche statistics." Journal of Statistical Mechanics: Theory and Experiment 2009, no. 01 (January 5, 2009): P01019. http://dx.doi.org/10.1088/1742-5468/2009/01/p01019.

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18

Deng, Jie, and Anter El-Azab. "Temporal statistics and coarse graining of dislocation ensembles." Philosophical Magazine 90, no. 27-28 (September 21, 2010): 3651–78. http://dx.doi.org/10.1080/14786435.2010.497472.

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19

Luft, Caroline Di Bernardi, Rosalind Baker, Aimee Goldstone, Yang Zhang, and Zoe Kourtzi. "Learning Temporal Statistics for Sensory Predictions in Aging." Journal of Cognitive Neuroscience 28, no. 3 (March 2016): 418–32. http://dx.doi.org/10.1162/jocn_a_00907.

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Predicting future events based on previous knowledge about the environment is critical for successful everyday interactions. Here, we ask which brain regions support our ability to predict the future based on implicit knowledge about the past in young and older age. Combining behavioral and fMRI measurements, we test whether training on structured temporal sequences improves the ability to predict upcoming sensory events; we then compare brain regions involved in learning predictive structures between young and older adults. Our behavioral results demonstrate that exposure to temporal sequences without feedback facilitates the ability of young and older adults to predict the orientation of an upcoming stimulus. Our fMRI results provide evidence for the involvement of corticostriatal regions in learning predictive structures in both young and older learners. In particular, we showed learning-dependent fMRI responses for structured sequences in frontoparietal regions and the striatum (putamen) for young adults. However, for older adults, learning-dependent activations were observed mainly in subcortical (putamen, thalamus) regions but were weaker in frontoparietal regions. Significant correlations of learning-dependent behavioral and fMRI changes in these regions suggest a strong link between brain activations and behavioral improvement rather than general overactivation. Thus, our findings suggest that predicting future events based on knowledge of temporal statistics engages brain regions involved in implicit learning in both young and older adults.

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20

Briassouli, A., and I. Kompatsiaris. "Robust Temporal Activity Templates Using Higher Order Statistics." IEEE Transactions on Image Processing 18, no. 12 (December 2009): 2756–68. http://dx.doi.org/10.1109/tip.2009.2029595.

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21

Buffett, B., and M. S. Avery. "How Does Temporal Resolution Influence Geomagnetic Reversal Statistics?" Geophysical Research Letters 46, no. 10 (May 20, 2019): 5146–52. http://dx.doi.org/10.1029/2019gl082976.

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22

Joyce, Daniel, Zoey Isherwood, and Michael Webster. "Spatial and temporal variations in natural scene statistics." Journal of Vision 22, no. 14 (December 5, 2022): 4481. http://dx.doi.org/10.1167/jov.22.14.4481.

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23

Camussi, R., and R. Verzicco. "Temporal statistics in high Rayleigh number convective turbulence." European Journal of Mechanics - B/Fluids 23, no. 3 (May 2004): 427–42. http://dx.doi.org/10.1016/j.euromechflu.2003.10.012.

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24

Micheloni, Christian, Lauro Snidaro, and Gian Luca Foresti. "Exploiting temporal statistics for events analysis and understanding." Image and Vision Computing 27, no. 10 (September 2009): 1459–69. http://dx.doi.org/10.1016/j.imavis.2008.07.005.

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25

Sparks, Ross. "Spatio-Temporal Surveillance Using CUSUM of Order Statistics." Quality and Reliability Engineering International 31, no. 8 (September 18, 2014): 1743–60. http://dx.doi.org/10.1002/qre.1714.

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26

Wikle, Christopher K. "Modern perspectives on statistics for spatio-temporal data." Wiley Interdisciplinary Reviews: Computational Statistics 7, no. 1 (November 20, 2014): 86–98. http://dx.doi.org/10.1002/wics.1341.

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27

Harvill, Jane L. "Spatio-temporal processes." Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 3 (April 28, 2010): 375–82. http://dx.doi.org/10.1002/wics.88.

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28

Ma, Chunsheng. "Semiparametric spatio-temporal covariance models with the ARMA temporal margin." Annals of the Institute of Statistical Mathematics 57, no. 2 (June 2005): 221–33. http://dx.doi.org/10.1007/bf02507023.

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29

Ridley, Michael. "Quantum Probability from Temporal Structure." Quantum Reports 5, no. 2 (June 12, 2023): 496–509. http://dx.doi.org/10.3390/quantum5020033.

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The Born probability measure describes the statistics of measurements in which observers self-locate themselves in some region of reality. In ψ-ontic quantum theories, reality is directly represented by the wavefunction. We show that quantum probabilities may be identified using fractions of a universal multiple-time wavefunction containing both causal and retrocausal temporal parts. This wavefunction is defined in an appropriately generalized history space on the Keldysh time contour. Our deterministic formulation of quantum mechanics replaces the initial condition of standard Schrödinger dynamics, with a network of ‘fixed points’ defining quantum histories on the contour. The Born measure is derived by summing up the wavefunction along these histories. We then apply the same technique to the derivation of the statistics of measurements with pre- and postselection.

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30

Ohira, Toru. "Temporal stochasticity and non-locality." Journal of Statistical Mechanics: Theory and Experiment 2009, no. 01 (January 6, 2009): P01032. http://dx.doi.org/10.1088/1742-5468/2009/01/p01032.

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31

Pasaribu, Ayodhia Pitaloka, Tsheten Tsheten, Muhammad Yamin, Yulia Maryani, Fahmi Fahmi, Archie C. A. Clements, Darren J. Gray, and Kinley Wangdi. "Spatio-Temporal Patterns of Dengue Incidence in Medan City, North Sumatera, Indonesia." Tropical Medicine and Infectious Disease 6, no. 1 (March 5, 2021): 30. http://dx.doi.org/10.3390/tropicalmed6010030.

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Dengue has been a perennial public health problem in Medan city, North Sumatera, despite the widespread implementation of dengue control. Understanding the spatial and temporal pattern of dengue is critical for effective implementation of dengue control strategies. This study aimed to characterize the epidemiology and spatio-temporal patterns of dengue in Medan City, Indonesia. Data on dengue incidence were obtained from January 2016 to December 2019. Kulldorff’s space-time scan statistic was used to identify dengue clusters. The Getis-Ord Gi* and Anselin Local Moran’s I statistics were used for further characterisation of dengue hotspots and cold spots. Results: A total of 5556 cases were reported from 151 villages across 21 districts in Medan City. Annual incidence in villages varied from zero to 439.32 per 100,000 inhabitants. According to Kulldorf’s space-time scan statistic, the most likely cluster was located in 27 villages in the south-west of Medan between January 2016 and February 2017, with a relative risk (RR) of 2.47. Getis-Ord Gi* and LISA statistics also identified these villages as hotpot areas. Significant space-time dengue clusters were identified during the study period. These clusters could be prioritized for resource allocation for more efficient prevention and control of dengue.

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32

Gebremichael, Mekonnen, and Witold F. Krajewski. "Effect of Temporal Sampling on Inferred Rainfall Spatial Statistics." Journal of Applied Meteorology 44, no. 10 (October 1, 2005): 1626–33. http://dx.doi.org/10.1175/jam2283.1.

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Abstract On the basis of temporally sampled data obtained from satellites, spatial statistics of rainfall can be estimated. In this paper, the authors compare the estimated spatial statistics with their “true” or ensemble values calculated using 5 yr of 15-min radar-based rainfall data at a spatial domain of 512 km × 512 km in the central United States. The authors conducted a Monte Carlo sampling experiment to simulate different sampling scenarios for variable sampling intervals and rainfall averaging periods. The spatial statistics used are the moments of spatial distribution of rainfall, the spatial scaling exponents, and the spatial cross correlations between the sample and ensemble rainfall fields. The results demonstrated that the expected value of the relative error in the mean rain-rate estimate is zero for rainfall averaged over 5 days or longer, better temporal sampling produces average fields that are “less noisy” spatially, an increase in the sampling interval causes the sampled rainfall to be increasingly less correlated with the true rainfall map, and the spatial scaling exponent estimators could give a bias of 40% or less. The results of this study provide a basis for understanding the impact of temporal statistics on inferred spatial statistics.

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33

Verbyla, A. P., J. De Faveri, D. M. Deery, and G. J. Rebetzke. "Modelling temporal genetic and spatio‐temporal residual effects for high‐throughput phenotyping data*." Australian & New Zealand Journal of Statistics 63, no. 2 (June 2021): 284–308. http://dx.doi.org/10.1111/anzs.12336.

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34

Vogel, John S., Ted Ognibene, Magnus Palmblad, and Paula Reimer. "Counting Statistics and Ion Interval Density in AMS." Radiocarbon 46, no. 3 (2004): 1103–9. http://dx.doi.org/10.1017/s0033822200033038.

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Confidence in the precisions of accelerator mass spectrometry (AMS) and decay measurements must be comparable for the application of the radiocarbon calibration to age determinations using both technologies. We confirmed the random nature of the temporal distribution of 14C ions in an AMS spectrometer for a number of sample counting rates and properties of the sputtering process. The temporal distribution of ion counts was also measured to confirm the applicability of traditional counting statistics.

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35

C. A. O. Vieira, N. T. Santos, A. P. S. Carneiro, and A. A. S. Balieiro. "BRAZILIAN AMAZONIA DEFORESTATION DETECTION USING SPATIO-TEMPORAL SCAN STATISTICS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences I-2 (July 11, 2012): 51–55. http://dx.doi.org/10.5194/isprsannals-i-2-51-2012.

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36

Wang, Rui, Yuan Shen, Peter Tino, Andrew E. Welchman, and Zoe Kourtzi. "Learning predictive statistics from temporal sequences: Dynamics and strategies." Journal of Vision 17, no. 12 (October 1, 2017): 1. http://dx.doi.org/10.1167/17.12.1.

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37

Morchen, F., A. Ultsch, M. Thies, and I. Lohken. "Modeling timbre distance with temporal statistics from polyphonic music." IEEE Transactions on Audio, Speech and Language Processing 14, no. 1 (January 2006): 81–90. http://dx.doi.org/10.1109/tsa.2005.860352.

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38

Lewis, G. F., and M. J. Irwin. "The statistics of microlensing light curves -- II. Temporal analysis." Monthly Notices of the Royal Astronomical Society 283, no. 1 (October 21, 1996): 225–40. http://dx.doi.org/10.1093/mnras/283.1.225.

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39

Conte, M. M., and J. D. Victor. "Temporal stability of image statistics in visual working memory." Journal of Vision 3, no. 9 (March 18, 2010): 680. http://dx.doi.org/10.1167/3.9.680.

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40

Gulati, K., B. L. Evans, and S. Srikanteswara. "Joint Temporal Statistics of Interference in Decentralized Wireless Networks." IEEE Transactions on Signal Processing 60, no. 12 (December 2012): 6713–18. http://dx.doi.org/10.1109/tsp.2012.2215025.

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41

Li, Nan-Nan, Ning Zhang, and Tao Zhou. "Empirical analysis on temporal statistics of human correspondence patterns." Physica A: Statistical Mechanics and its Applications 387, no. 25 (November 2008): 6391–94. http://dx.doi.org/10.1016/j.physa.2008.07.021.

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42

Dimitriou, Loukas, Paraskevas Nikolaou, and Constantinos Antoniou. "Exploring the temporal stability of global road safety statistics." Accident Analysis & Prevention 130 (September 2019): 38–53. http://dx.doi.org/10.1016/j.aap.2017.12.015.

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43

Sansó, Bruno. "Noel Cressie, Chris Wikle: Statistics for Spatio-Temporal Data." Mathematical Geosciences 45, no. 2 (November 9, 2012): 253–54. http://dx.doi.org/10.1007/s11004-012-9430-5.

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44

Bonnet, Pierre, Mathilde Bonnefond, and Anne Kösem. "What is a Rhythm for the Brain? The Impact of Contextual Temporal Variability on Auditory Perception." Journal of Cognition 7, no. 1 (January 17, 2024): 15. http://dx.doi.org/10.5334/joc.344.

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Temporal predictions can be formed and impact perception when sensory timing is fully predictable: for instance, the discrimination of a target sound is enhanced if it is presented on the beat of an isochronous rhythm. However, natural sensory stimuli, like speech or music, are not entirely predictable, but still possess statistical temporal regularities. We investigated whether temporal expectations can be formed in non-fully predictable contexts, and how the temporal variability of sensory contexts affects auditory perception. Specifically, we asked how “rhythmic” an auditory stimulation needs to be in order to observe temporal predictions effects on auditory discrimination performances. In this behavioral auditory oddball experiment, participants listened to auditory sound sequences where the temporal interval between each sound was drawn from gaussian distributions with distinct standard deviations. Participants were asked to discriminate sounds with a deviant pitch in the sequences. Auditory discrimination performances, as measured with deviant sound discrimination accuracy and response times, progressively declined as the temporal variability of the sound sequence increased. Moreover, both global and local temporal statistics impacted auditory perception, suggesting that temporal statistics are promptly integrated to optimize perception. Altogether, these results suggests that temporal predictions can be set up quickly based on the temporal statistics of past sensory events and are robust to a certain amount of temporal variability. Therefore, temporal predictions can be built on sensory stimulations that are not purely periodic nor temporally deterministic.

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45

Iyer, V., S. Shetty, and S. S. Iyengar. "STATISTICAL METHODS IN AI: RARE EVENT LEARNING USING ASSOCIATIVE RULES AND HIGHER-ORDER STATISTICS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-4/W2 (July 10, 2015): 119–30. http://dx.doi.org/10.5194/isprsannals-ii-4-w2-119-2015.

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Rare event learning has not been actively researched since lately due to the unavailability of algorithms which deal with big samples. The research addresses spatio-temporal streams from multi-resolution sensors to find actionable items from a perspective of real-time algorithms. This computing framework is independent of the number of input samples, application domain, labelled or label-less streams. A sampling overlap algorithm such as Brooks-Iyengar is used for dealing with noisy sensor streams. We extend the existing noise pre-processing algorithms using Data-Cleaning trees. Pre-processing using ensemble of trees using bagging and multi-target regression showed robustness to random noise and missing data. As spatio-temporal streams are highly statistically correlated, we prove that a temporal window based sampling from sensor data streams converges after n samples using Hoeffding bounds. Which can be used for fast prediction of new samples in real-time. The Data-cleaning tree model uses a nonparametric node splitting technique, which can be learned in an iterative way which scales linearly in memory consumption for any size input stream. The improved task based ensemble extraction is compared with non-linear computation models using various SVM kernels for speed and accuracy. We show using empirical datasets the explicit rule learning computation is linear in time and is only dependent on the number of leafs present in the tree ensemble. The use of unpruned trees (t) in our proposed ensemble always yields minimum number (m) of leafs keeping pre-processing computation to n × t log m compared to N2 for Gram Matrix. We also show that the task based feature induction yields higher Qualify of Data (QoD) in the feature space compared to kernel methods using Gram Matrix.

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46

Kovanen, Lauri, Márton Karsai, Kimmo Kaski, János Kertész, and Jari Saramäki. "Temporal motifs in time-dependent networks." Journal of Statistical Mechanics: Theory and Experiment 2011, no. 11 (November 11, 2011): P11005. http://dx.doi.org/10.1088/1742-5468/2011/11/p11005.

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47

Campbell, N. A., and H. T. Kiiveri. "Spectral-Temporal Indices for Discrimination." Applied Statistics 37, no. 1 (1988): 51. http://dx.doi.org/10.2307/2347493.

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48

Spiegel, Elmar, Thomas Kneib, and Fabian Otto-Sobotka. "Spatio-temporal expectile regression models." Statistical Modelling 20, no. 4 (March 18, 2019): 386–409. http://dx.doi.org/10.1177/1471082x19829945.

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Spatio-temporal models are becoming increasingly popular in recent regression research. However, they usually rely on the assumption of a specific parametric distribution for the response and/or homoscedastic error terms. In this article, we propose to apply semiparametric expectile regression to model spatio-temporal effects beyond the mean. Besides the removal of the assumption of a specific distribution and homoscedasticity, with expectile regression the whole distribution of the response can be estimated. For the use of expectiles, we interpret them as weighted means and estimate them by established tools of (penalized) least squares regression. The spatio-temporal effect is set up as an interaction between time and space either based on trivariate tensor product P-splines or the tensor product of a Gaussian Markov random field and a univariate P-spline. Importantly, the model can easily be split up into main effects and interactions to facilitate interpretation. The method is presented along the analysis of spatio-temporal variation of temperatures in Germany from 1980 to 2014.

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Sudakin, Daniel, and Laura E. Power. "Regional and temporal variation in methamphetamine-related incidents: applications of spatial and temporal scan statistics." Clinical Toxicology 47, no. 3 (March 2009): 243–47. http://dx.doi.org/10.1080/15563650802516160.

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Kaptué, Armel T., Niall P. Hanan, Lara Prihodko, and Jorge A. Ramirez. "Spatial and temporal characteristics of rainfall in A frica: S ummary statistics for temporal downscaling." Water Resources Research 51, no. 4 (April 2015): 2668–79. http://dx.doi.org/10.1002/2014wr015918.

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Journal articles on the topic 'Temporal statistics'

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