Relevant bibliographies by topics / Temporal statistics

Academic literature on the topic 'Temporal statistics'

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Published: 24 February 2024

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Journal articles on the topic "Temporal statistics"

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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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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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Dissertations / Theses on the topic "Temporal statistics"

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Venkatasubramaniam, Ashwini Kolumam. "Nonparametric clustering for spatio-temporal data." Thesis, University of Glasgow, 2019. http://theses.gla.ac.uk/40957/.

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Clustering algorithms attempt the identification of distinct subgroups within heterogeneous data and are commonly utilised as an exploratory tool. The definition of a cluster is dependent on the relevant dataset and associated constraints; clustering methods seek to determine homogeneous subgroups that each correspond to a distinct set of characteristics. This thesis focuses on the development of spatial clustering algorithms and the methods are motivated by the complexities posed by spatio-temporal data. The examples in this thesis primarily come from spatial structures described in the context of traffic modelling and are based on occupancy observations recorded over time for an urban road network. Levels of occupancy indicate the extent of traffic congestion and the goal is to identify distinct regions of traffic congestion in the urban road network. Spatial clustering for spatio-temporal data is an increasingly important research problem and the challenges posed by such research problems often demand the development of bespoke clustering methods. Many existing clustering algorithms, with a focus on accommodating the underlying spatial structure, do not generate clusters that adequately represent differences in the temporal pattern across the network. This thesis is primarily concerned with developing nonparametric clustering algorithms that seek to identify spatially contiguous clusters and retain underlying temporal patterns. Broadly, this thesis introduces two clustering algorithms that are capable of accommodating spatial and temporal dependencies that are inherent to the dataset. The first is a functional distributional clustering algorithm that is implemented within an agglomerative hierarchical clustering framework as a two-stage process. The method is based on a measure of distance that utilises estimated cumulative distribution functions over the data and this unique distance is both functional and distributional. This notion of distance utilises the differences in densities to identify distinct clusters in the graph, rather than raw recorded observations. However, distinct characteristics may not necessarily be identified and distinguishable by a densities-based distance measure, as defined within the agglomerative hierarchical clustering framework. In this thesis, we also introduce a formal Bayesian clustering approach that enables the researcher to determine spatially contiguous clusters in a data-driven manner. This framework varies from the set of assumptions introduced by the functional distributional clustering algorithm. This flexible Bayesian model employs a binary dependent Chinese restaurant process (binDCRP) to place a prior over the geographical constraints posed by a graph-based network. The binDCRP is a special case of the distance dependent Chinese restaurant process that was first introduced by Blei and Frazier (2011); the binDCRP is modified to account for data that poses spatial constraints. The binDCRP seeks to cluster data such that adjacent or neighbouring regions in a spatial structure are more likely to belong to the same cluster. The binDCRP introduces a large number of singletons within the spatial structure and we modify the binDCRP to enable the researcher to restrict the number of clusters in the graph. It is also reasonable to assume that individual junctions within a cluster are spatially correlated to adjacent junctions, due to the nature of traffic and the spread of congestion. In order to fully account for spatial correlation within a cluster structure, the model utilises a type of the conditional auto-regressive (CAR) model. The model also accounts for temporal dependencies using a first order auto-regressive (AR-1) model. In this mean-based flexible Bayesian model, the data is assumed to follow a Gaussian distribution and we utilise Kronecker product identities within the definition of the spatio-temporal precision matrix to improve the computational efficiency. The model utilises a Metropolis within Gibbs sampler to fully explore all possible partition structures within the network and infer the relevant parameters of the spatio-temporal precision matrix. The flexible Bayesian method is also applicable to map-based spatial structures and we describe the model in this context as well. The developed Bayesian model is applied to a simulated spatio-temporal dataset that is composed of three distinct known clusters. The differences in the clusters are reflected by distinct mean values over time associated with spatial regions. The nature of this mean-based comparison differs from the functional distributional clustering approach that seeks to identify differences across the distribution. We demonstrate the ability of the Bayesian model to restrict the number of clusters using a simulated data structure with distinctly defined clusters. The sampler is also able to explore potential cluster structures in an efficient manner and this is demonstrated using a simulated spatio-temporal data structure. The performance of this model is illustrated by an application to a dataset over an urban road network that presents traffic as a process varying continuously across space and time. We also apply this model to an areal unit dataset composed of property prices over a period of time for the Avon county in England.
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Barry, Jon. "Spatial and temporal statistics in the environmental sciences." Thesis, Lancaster University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337435.

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Richardson, Jennifer. "Topics in statistics of spatial-temporal disease modelling." Thesis, Durham University, 2009. http://etheses.dur.ac.uk/2122/.

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This thesis is concerned with providing further statistical development in the area of space-time modelling with particular application to disease data. We briefly consider the non-Bayesian approaches of empirical mode decomposition and generalised linear modelling for analysing space-time data, but our main focus is on the increasingly popular Bayesian hierarchical approach and topics surrounding that. We begin by introducing the hierarchical Poisson regression model of Mugglin et al. [36] and a data set provided by NHS Direct which will be used to illustrate our results through-out the remainder of the thesis. We provide details of how a Bayesian analysis can be performed using Markov chain Monte Carlo (MCMC) via the software LinBUGS then go on to consider two particular issues associated with such analyses. Firstly, a problem with the efficiency of MCMC for the Poisson regression model is likely to be due to the presence of non-standard conditional distributions. We develop and test the 'improved auxiliary mixture sampling' method which introduces auxiliary variables to the conditional distribution in such a way that it becomes multivariate Normal and an efficient block Gibbs sampling scheme can be used to simulate from it. Secondly, since MCMC allows modelling of such complexity, inputs such as priors can only be elicited in a casual way thereby increasing the need to check how sensitive our output is to changes to the prior. We therefore develop and test the 'marginal sensitivity' method which, using only one MCMC output sample, quantifies how sensitive the marginal posterior distributions are to changes to prior parameters
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Wright, Dean. "Temporal phase and amplitude statistics in coherent radiation." Thesis, University of Nottingham, 2005. http://eprints.nottingham.ac.uk/12126/.

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Interest in coherent remote sensing systems has stimulated investigations in the properties laser propagation through extended atmospheric turbulence. This thesis investigates the statistics of phase, and phase related, observables using analytical and computational techniques, together with experimental results. The phase screen technique is used to simulate perturbations to the refractive index of a medium through which the radiation propagates. Several different turbulence models (Gaussian correlated noise, Kolmogorov turbulence, Tatarski and Von Karman spectral models) are investigated, and their relative merits for describing experimental conditions and descriptive statistical measures are compared and contrasted. The phase power spectrum is crucial to an understanding of the practical operation of a coherent imaging system, and later part of the thesis is devoted to the investigation of a LIDAR system in particular. Several turbulence regimes are investigated, from an analytical treatment of a weakly turbulent, extended atmosphere, to large 3D computations designed to simulate experimental arrangements. The 3D simulation technique presented herein has been developed to allow for the investigation of temporal statistics. New power law behaviours are found to appear in temporal frequency spectra which differ from the -8/3 power law form that has been accepted in much of the literature. Strongly turbulent regimes result in a -2 power law while the use of a Gaussian beam profile in an extended medium gives a -11/3 power law under weak turbulence conditions. Please note: Pagination in electronic reproduction differs from print original. The print version is the version of record.
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D'ANGELO, Nicoletta. "Local methods for complex spatio-temporal point processes." Doctoral thesis, Università degli Studi di Palermo, 2022. https://hdl.handle.net/10447/574349.

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White, Gentry. "Bayesian semiparametric spatial and joint spatio-temporal modeling." Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4450.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2006.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (May 2, 2007) Vita. Includes bibliographical references.
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Arab, Ali. "Hierarchical spatio-temporal models for environmental processes." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4698.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed Nov. 21, 2007). Vita. Includes bibliographical references.
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Slack, Marc G. "Spatial and temporal path planning." Thesis, This resource online, 1987. http://scholar.lib.vt.edu/theses/available/etd-04272010-020255/.

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Clifford, Sam. "Spatio-temporal modelling of ultrafine particle number concentration." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/63528/4/Samuel_Clifford_Thesis.pdf.

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This thesis developed semi-parametric regression models for estimating the spatio-temporal distribution of outdoor airborne ultrafine particle number concentration (PNC). The models developed incorporate multivariate penalised splines and random walks and autoregressive errors in order to estimate non-linear functions of space, time and other covariates. The models were applied to data from the "Ultrafine Particles from Traffic Emissions and Child" project in Brisbane, Australia, and to longitudinal measurements of air quality in Helsinki, Finland. The spline and random walk aspects of the models reveal how the daily trend in PNC changes over the year in Helsinki and the similarities and differences in the daily and weekly trends across multiple primary schools in Brisbane. Midday peaks in PNC in Brisbane locations are attributed to new particle formation events at the Port of Brisbane and Brisbane Airport.
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Foley, Kristen Madsen. "Multivariate Spatial Temporal Statistical Models for Applications in Coastal Ocean Prediction." NCSU, 2006. http://www.lib.ncsu.edu/theses/available/etd-07042006-110351/.

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Estimating the spatial and temporal variation of surface wind fields plays an important role in modeling atmospheric and oceanic processes. This is particularly true for hurricane forecasting, where numerical ocean models are used to predict the height of the storm surge and the degree of coastal flooding. We use multivariate spatial-temporal statistical methods to improve coastal storm surge prediction using disparate sources of observation data. An Ensemble Kalman Filter is used to assimilate water elevation into a three dimension primitive equations ocean model. We find that data assimilation is able to improve the estimates for water elevation for a case study of Hurricane Charley of 2004. In addition we investigate the impact of inaccuracies in the wind field inputs which are the main forcing of the numerical model in storm surge applications. A new multivariate spatial statistical framework is developed to improve the estimation of these wind inputs. A spatial linear model of coregionalization (LMC) is used to account for the cross-dependency between the two orthogonal wind components. A Bayesian approach is used for estimation of the parameters of the multivariate spatial model and a physically based wind model while accounting for potential additive and multiplicative bias in the observed wind data. This spatial model consistently improves parameter estimation and prediction for surface wind data for the Hurricane Charley case study when compared to the original physical wind model. These methods are also shown to improve storm surge estimates when used as the forcing fields for the coastal ocean model. Finally we describe a new framework for estimating multivariate nonstationary spatial-temporal processes based on an extension of the LMC model. We compare this approach to other multivariate spatial models and describe an application to surface wind fields from Hurricane Floyd of 1999.
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Books on the topic "Temporal statistics"

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Wikle, Christopher K., Andrew Zammit-Mangion, and Noel Cressie. Spatio-Temporal Statistics with R. Boca Raton, Florida : CRC Press, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781351769723.

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Cressie, Noel A. C. Statistics for spatio-temporal data. Hoboken, N.J: Wiley, 2011.

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Sherman, Michael. Spatial Statistics and Spatio-Temporal Data. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470974391.

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Stock, Oliviero. Spatial and Temporal Reasoning. Dordrecht: Springer, 1997.

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1966-, Bogaert Patrick, and Serre Marc L. 1967-, eds. Temporal GIS: Advanced functions for field-based applications. Berlin: Springer, 2001.

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Julio, Segura, ed. Análisis de la contratación temporal en España. Madrid: Ministerio de Trabajo y Seguridad Social, 1991.

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Mahan, Robert P. Circular statistical methods: Applications in spatial and temporal performance analysis. Alexandria, Va: U.S. Army Research Institute for the Behavioral and Social Sciences, 1991.

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Xinming, Tang, ed. Advances in spatio-temporal analysis. London: Taylor & Francis, 2008.

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Ligozat, Gérard. Qualitative spatial and temporal reasoning. London, UK: ISTE, 2011.

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Spatial statistics and spatio-temporal data: Covariance functions and directional properties. Chichester, West Sussex, U.K: Wiley, 2011.

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Book chapters on the topic "Temporal statistics"

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Hooten, Mevin B., Devin S. Johnson, Brett T. McClintock, and Juan M. Morales. "Statistics for Temporal Data." In Animal Movement, 55–98. Boca Raton : CRC Press, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315117744-3.

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Wikle, Christopher K., Andrew Zammit-Mangion, and Noel Cressie. "Introduction to Spatio-Temporal Statistics." In Spatio-Temporal Statistics with R, 1–16. Boca Raton, Florida : CRC Press, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781351769723-1.

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Wikle, Christopher K., Andrew Zammit-Mangion, and Noel Cressie. "Spatio-Temporal Statistical Models." In Spatio-Temporal Statistics with R, 77–136. Boca Raton, Florida : CRC Press, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781351769723-3.

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Wikle, Christopher K., Andrew Zammit-Mangion, and Noel Cressie. "Exploring Spatio-Temporal Data." In Spatio-Temporal Statistics with R, 17–76. Boca Raton, Florida : CRC Press, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781351769723-2.

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Wikle, Christopher K., Andrew Zammit-Mangion, and Noel Cressie. "Dynamic Spatio-Temporal Models." In Spatio-Temporal Statistics with R, 205–52. Boca Raton, Florida : CRC Press, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781351769723-5.

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Forzani, Liliana, Ricardo Fraiman, and Pamela Llop. "Density Estimation for Spatial-Temporal Data." In Contributions to Statistics, 117–21. Heidelberg: Physica-Verlag HD, 2011. http://dx.doi.org/10.1007/978-3-7908-2736-1_18.

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Tango, Toshiro. "Tests for Temporal Clustering." In Statistics for Biology and Health, 49–70. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-1572-6_4.

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Wikle, Christopher K., Andrew Zammit-Mangion, and Noel Cressie. "Descriptive Spatio-Temporal Statistical Models." In Spatio-Temporal Statistics with R, 137–204. Boca Raton, Florida : CRC Press, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781351769723-4.

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Wikle, Christopher K., Andrew Zammit-Mangion, and Noel Cressie. "Evaluating Spatio-Temporal Statistical Models." In Spatio-Temporal Statistics with R, 253–302. Boca Raton, Florida : CRC Press, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781351769723-6.

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Bar-Hen, Avner, Liliane Bel, and Rachid Cheddadi. "Spatio-temporal Functional Regression on Paleoecological Data." In Contributions to Statistics, 54–56. Heidelberg: Physica-Verlag HD, 2008. http://dx.doi.org/10.1007/978-3-7908-2062-1_9.

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Conference papers on the topic "Temporal statistics"

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Boldt, Martin, and Anton Borg. "A Statistical Method for Detecting Significant Temporal Hotspots Using LISA Statistics." In 2017 European Intelligence and Security Informatics Conference (EISIC). IEEE, 2017. http://dx.doi.org/10.1109/eisic.2017.24.

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Shu, Hong, Chao Zhao, and Aiping Xu. "Spatio-temporal statistics for exploratory NDVI image analysis." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838576.

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Liu, Xiaoming, and Tsuhan Chen. "Shot boundary detection using temporal statistics modeling." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5745381.

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Xiaoming Liu and Tsuhan Chen. "Shot boundary detection using temporal statistics modeling." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1004639.

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Hamzah, Firdaus Mohamad, Rofizah Mohammad, Mohd Kamal Mohd Nawawi, Mohd Tahir Ismail, and Norazman Arbin. "Temporal changes in nutrients in freshwater Scottish lake." In STATISTICS AND OPERATIONAL RESEARCH INTERNATIONAL CONFERENCE (SORIC 2013). AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4894362.

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Marshall, S., L. Yu, Y. Xiao, and E. Dougherty. "Temporal inference of probabilistic Boolean networks." In 2006 IEEE International Workshop on Genomic Signal Processing and Statistics. IEEE, 2006. http://dx.doi.org/10.1109/gensips.2006.353161.

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Erfani Joorabchi, Minoo, Ji-Dong Yim, and Christopher D. Shaw. "EmailTime: visual analytics and statistics for temporal email." In IS&T/SPIE Electronic Imaging. SPIE, 2011. http://dx.doi.org/10.1117/12.871712.

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Shijian Tang, Xuedan Zhang, and Yuhan Dong. "Temporal statistics of irradiance in moving turbulent ocean." In 2013 MTS/IEEE OCEANS. IEEE, 2013. http://dx.doi.org/10.1109/oceans-bergen.2013.6607967.

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Micheloni, Christian, Lauro Snidaro, Claudio Piciarelli, and Gian Luca Foresti. "Exploiting Temporal Statistics for Events Analysis and Understanding." In 14th International Conference on Image Analysis and Processing (ICIAP 2007). IEEE, 2007. http://dx.doi.org/10.1109/iciap.2007.4362832.

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Wulandari, Ita, Anwar Fitrianto, Anik Djuraidah, and I. Made Sumertajaya. "Spatio temporal random effect models for child labor mapping." In INTERNATIONAL CONFERENCE ON STATISTICS AND DATA SCIENCE 2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0108018.

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Reports on the topic "Temporal statistics"

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Oberer, Richard B. Fission Multiplicity Detection with Temporal Gamma-Neutron Discrimination from Higher-Order Time Correlation Statistics. Office of Scientific and Technical Information (OSTI), October 2002. http://dx.doi.org/10.2172/808851.

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Mattingly, J. K. Multivariate High Order Statistics of Measurements of the Temporal Evolution of Fission Chain-Reactions. Office of Scientific and Technical Information (OSTI), March 2001. http://dx.doi.org/10.2172/814036.

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Oberer, R. B. Fission Multiplicity Detection With Temporal Gamma-Neutron Discrimination From Higher Order Time Correlation Statistics. Office of Scientific and Technical Information (OSTI), January 2002. http://dx.doi.org/10.2172/814311.

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Wikle, Christopher K. Spatio-temporal statistical models with applications to atmospheric processes. Office of Scientific and Technical Information (OSTI), January 1996. http://dx.doi.org/10.2172/587661.

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Davis, Adam Christopher, and Steven Richard Booth. Statistical Analysis of Demographic and Temporal Differences in LANL's 2014 Voluntary Protection Program Survey. Office of Scientific and Technical Information (OSTI), August 2015. http://dx.doi.org/10.2172/1212618.

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Faybishenko, Boris, Bhavna Arora, Dipankar Dwivedi, and Eoin Brodie. Statistical Framework to Assess the Temporal and Spatial Climate Changes: East River Mountainous Watershed case study. Office of Scientific and Technical Information (OSTI), December 2021. http://dx.doi.org/10.2172/1987500.

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Ziesler, Pamela, and Claire Spalding. Statistical abstract: 2021. National Park Service, May 2022. http://dx.doi.org/10.36967/nrds-2293345.

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In 2021, recreation visits to National Park Service (NPS) sites rebounded from the COVID-19 pandemic-driven low visitation of 2020 and climbed to 297,115,406 recreation visits. This is an increase of 60 million recreation visits (+25.3%) from 2020 and a decrease of 30 million recreation visits (-9.3%) from 2019. Recreation visitor hours were 1,356,657,749 – a 28.6% increase from 2020 and a 5.1% decrease from 2019. Total overnight stays followed a similar pattern with 12,745,455 overnight stays – up 4.7 million (+58.5%) from 2020 and down 1.1 million (-8%) from 2019. Five parks were added to the reporting system in 2021: Alagnak Wild River in Alaska, Camp Nelson National Monument in Kentucky, Medgar and Myrlie Evers Home National Monument in Mississippi, Tule Springs Fossil Beds National Monument in Nevada, and World War I Memorial in Washington, D.C. These parks were responsible for over 629,000 recreation visits in 2021. Factors influencing visits to National Park System units in 2021 include: continuing closures and limited capacities due to COVID-19 mitigation at some parks, temporary closures for wildland fires in 2021 (eleven parks), severe regional smoke/haze from ongoing wildland fires throughout the summer and early autumn affecting parks in the western half and northern tier of states in the continental U.S., two hurricanes in 2021 – both in August – impacted visitation: Hurricane Henri caused temporary closures of some parks in the northeast and Hurricane Ida caused temporary closures of parks along the Gulf Coast and generated some heavy flooding in the northeast, hurricanes and wildland fires in previous years resulting in lingering closures, most notably Hurricanes Irma and Maria in 2017, the Carr and Woolsey Fires in 2018, Hurricane Dorian in 2019, the Caldwell, Cameron Peak, East Troublesome, and Woodward Fires in 2020, and Hurricane Sally in 2020. Forty-four parks set a record for recreation visits in 2021 and 6 parks broke a record they set in 2020. See Appendix A for a list of record parks. The number of reporting units with over 10 million recreation visits was the same as in recent years (3 parks) and 73 parks had over 1 million recreation visits. Twenty-five percent of total recreation visits occurred in the top 8 parks and fifty percent of total visitation occurred in the top 25 parks. Several parks passed annual visitation milestones including Capulin Volcano NM which passed 100,000 annual recreation visits for the first time, Big Bend NP and Devils Tower NM which each passed 500,000 annual recreation visits for the first time, and Zion NP which passed 5 million visits for the first time. Other parks passed milestones for accumulated recreation visits including Hamilton Grange NMEM (1968-2021) and Palo Alto Battlefield NHP (2003-2021) each passing 1 million total recreation visits, Voyageurs NP (1976-2021) passing 10 million total recreation visits, and Hot Springs NP (1904-2021) passing 100 million total recreation visits. Population center designations were updated in 2021 to reflect overlap of park boundaries with statistical areas from the 2020 U.S. Census. Many population center changes reflect increases in local population as indicated by parks changing from rural to outlying or from outlying to suburban. Other changes reflect increasing complexity in population density as parks changed from a single designation, such as rural or suburban, to a mixed designation. See the Definitions section for population center definitions and Table B.1 for previous and updated population center designations by park. In the pages that follow, a series of tables and figures display visitor use data for calendar year 2021. By documenting these visits across the National Park System, the NPS Statistical Abstract offers a historical record of visitor use in parks and provides NPS staff and partners with a useful tool for effective management and planning. In 2021, 394 of 423 NPS units...
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8

Workman, Austin, and Jay Clausen. Meteorological property and temporal variable effect on spatial semivariance of infrared thermography of soil surfaces for detection of foreign objects. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41024.

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The environmental phenomenological properties responsible for the thermal variability evident in the use of thermal infrared (IR) sensor systems is not well understood. The research objective of this work is to understand the environmental and climatological properties contributing to the temporal and spatial thermal variance of soils. We recorded thermal images of surface temperature of soil as well as several meteorological properties such as weather condition and solar irradiance of loamy soil located at the Cold Regions Research and Engineering Lab (CRREL) facility. We assessed sensor performance by analyzing how recorded meteorological properties affected the spatial structure by observing statistical differences in spatial autocorrelation and dependence parameter estimates.
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Elton, Nicholas, Laura Hayes, and Joanna Wozniak-Brown. Preliminary Results: Emergency Shelter and Cooling Center Practices in Connecticut. UConn Connecticut Institute for Resilience and Climate Adaptation, May 2022. http://dx.doi.org/10.56576/idwm7145.

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Researchers with the Connecticut Department of Public Health and University of Connecticut Connecticut Institute for Resilience and Climate Adaptation conducted a survey in fall of 2020 on temporary emergency shelters and cooling centers practices in Connecticut. The primary survey objective was to organize and analyze information to inform public health officials and climate resilience planners in the development of best management practices of cooling centers and temporary emergency shelters, as first recommended by the Governor's Council on Climate Change. A lack of statistical significance limited the researchers' ability to make broad state-wide practice observations; however, there were limitations in the amenities offered at shelters and cooling centers, public advertisements were focus on online methods, and health departments were slightly more involved in emergency sheltering over cooling centers, among respondents. Key considerations for future policy and resilience planning are offered.
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Johansen, Richard, Alan Katzenmeyer, Kaytee Pokrzywinski, and Molly Reif. A review of sensor-based approaches for monitoring rapid response treatments of cyanoHABs. Engineer Research and Development Center (U.S.), July 2023. http://dx.doi.org/10.21079/11681/47261.

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Water quality sensors are dynamic and vary greatly both in terms of utility and data acquisition. Data collection can range from single-parameter and one-dimensional to highly complex multiparameter spatiotemporal. Likewise, the analytical and statistical approaches range from relatively simple (e.g., linear regression) to more complex (e.g., artificial neural networks). Therefore, the decision to implement a particular water quality monitoring strategy is dependent upon many factors and varies widely. The purpose of this review was to document the current scientific literature to identify and compile approaches for water quality monitoring as well as statistical methodologies required to analyze and visualize highly diverse spatiotemporal water quality data. The literature review identified two broad categories: (1) sensor-based approaches for monitoring rapid response treatments of cyanobacterial harmful algal blooms (cyanoHABs), and (2) analytical tools and techniques to analyze complex high resolution spatial and temporal water quality data. The ultimate goal of this review is to provide the current state of the science as an array of scalable approaches, spanning from simple and practical to complex and comprehensive, and thus, equipping the US Army Corps of Engineers (USACE) water quality managers with options for technology-analysis combinations that best fit their needs.
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