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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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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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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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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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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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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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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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Zheng, Wenjun. "Wavelet-based estimation for Gaussian time series and spatio-temporal processes." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1405608102.
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O'Donnell, David. "Spatial prediction and spatio-temporal modelling on river networks." Thesis, University of Glasgow, 2012. http://theses.gla.ac.uk/3161/.
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The application of existing geostatistical theory to the context of stream networks provides a number of interesting and challenging problems. The most important of these is how to adapt existing theory to allow for stream, as opposed to Euclidean, distance to be used. Valid stream distance based models for the covariance structure have been denied in the literature, and this thesis explores the use of such models using data from the River Tweed. The data span a period of twenty-one years, beginning in 1986. During this time period, up to eighty-three stations are monitored for a variety of chemical and biological determinands. This thesis will focus on nitrogen, a key nutrient in determining water quality, especially given the Nitrates Directive (adopted in 1991) and the Water Framework Directive(adopted in 2002). These are European Union legislations that have set legally enforcable guidelines for controlling pollution which national bodies must comply with. The focus of analysis is on several choices that must be made in order to carry out spatial prediction on a river network. The role of spatial trend, whether it be based on stream or Euclidean distance, is discussed and the impact of the bandwidth of the estimate of nonparametric trend is explored. The stream distance based "tail-up" covariance model structure of Ver Hoef and Peterson (2010) is assessed and combined with a standard Euclidean distance based structure to form a mixture model. This is then evaluated using crossvalidation studies in order to determine the optimum mixture of the two covariance models for the data. Finally, the covariance models used for each of the elements of the mixture model are explored to determine the impact they have on the lowest root mean squared error, and the mixing proportion at which it is found. Using the predicted values at unobserved locations on the River Tweed, the distribution of yearly averaged nitrate levels around the river network is predicted and evaluated. Changes through the 21 years of data are noted and areas exceeding the limits set by the Nitrates Directive are highlighted. The differences in fitted values caused by using stream or Euclidean distance are evident in these predictions. The data is then modelled through space and time using additive models. A novel smoothing function for the spatial trend is defined. It is adapted from the tail-up model in order to retain its core features of flow connectivity and flow volume based weightings, in addition to being based on stream distance. This is then used to model all of the River Tweed data through space and time and identify temporal trends and seasonal patterns at different locations on the river.
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Yin, Jiangyong. "Bayesian Analysis of Non-Gaussian Stochastic Processes for Temporal and Spatial Data." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1406928537.
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Bonnet, Pierre. "Impact of temporal statistics on the processing of auditory stimuli." Electronic Thesis or Diss., Lyon 1, 2024. http://www.theses.fr/2024LYO10246.
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Les régularités temporelles du contexte sont connues pour affecter la perception d'un prochain événement sensoriel. Par exemple, lorsque nous écoutons un métronome, nous pouvons facilement anticiper quand le prochain son se produira, ce qui améliore notre capacité à détecter des changements acoustiques subtils. Cependant, l’impact de la variabilité temporelle sur les mécanismes de prédiction reste mal compris. Cette question est cruciale car, d'un point de vue écologique, dans la musique et la parole en particulier, les événements sensoriels suivent plutôt des motifs réguliers mais dans lesquels peuvent également se produire une certaine variabilité temporelle. Dans cette thèse, nous avons étudié l'impact de la variabilité temporelle des séquences sonores sur la perception auditive, les réponses neuronales associées et leur impact potentiel sur le traitement du langage. Dans une première étude comportementale, nous avons réalisé une expérience dans laquelle les volontaires ont écouté différentes séquences sonores où l'intervalle temporel entre chaque son était tiré de distributions gaussiennes avec des écarts-types distincts. Nous avons établi que les prédictions temporelles dans des contextes probabilistes sont possibles et qu'elles diminuent progressivement à mesure que la variabilité temporelle du contexte augmente. Dans une seconde étude EEG, nous montrons que la variabilité temporelle du contexte influence la réponse évoquée aux sons. En effet, dans des séquences sonores plus régulières, la réponse aux sons cible présente une activité évoquée précoce, une amplitude MMN plus élevées, et une réponse P300 plus importante. Les résultats confirment les théories actuelles liant la dynamique d'entraînement neuronal aux mécanismes de prédiction temporelle : les périodes où l'entraînement neuronal était élevé étaient associées à une discrimination plus rapide des sons cibles. Enfin, dans la troisième partie de cette thèse, nous avons montré un déficit des mécanismes de prédiction temporelle dans la dyslexie. En utilisant le même paradigme que dans le premier chapitre expérimental, nous avons montré que les volontaires dyslexiques ont significativement plus de difficultés que des volontaires témoins appariés à discriminer des sons dans des séquences temporelles régulières. Dans l'ensemble, cette thèse fournit des informations sur les mécanismes de prédiction temporelle dans des contextes probabilistes et discute de leur impact potentiel sur le traitement du langageThe temporal regularities in the sensory context are known to affect the perception of an upcoming sensory event. For instance, when listening to a metronome, we can readily predict when the next sound will occur, which enhances our ability to detect subtle acoustic changes due to this anticipation. However, how the temporal variability can impact temporal prediction mechanisms remain poorly understood. This question is crucial because, from a naturalistic point of view, in music and speech in particular, sensory events rather follow patterns of temporal regularity and thus may also occur with a certain amount of temporal variability. In this thesis, we investigated how temporal variability of sound sequences impacts auditory perception, associated neural responses, and their potential impact on language processing. In a first behavioral study, we used an auditory oddball experiment in which participants listened to different sound sequences where the temporal interval between each sound was drawn from gaussian distributions with distinct standard deviations. We established that temporal predictions in probabilistic contexts are still possible and progressively declined as the temporal variability in the context increase. In a second EEG study, we show that temporal variability in context influences the evoked response to sounds as more regular sound sequence showed stronger ramping activity post-target onset, higher MMN amplitude and increased P300 response. The results further support current theories linking observed neural entrainment dynamics to temporal predictions mechanisms: periods where neural entrainment was high was associated with faster target sounds discrimination. Finally, in the third part of this thesis we showed a deficit in temporal prediction mechanisms in dyslexia. Using the same paradigm as in the first experimental chapter, dyslexic participants had significantly more difficulty discriminating sounds in regular temporal sequences than matched controls. Overall, this thesis provides insights into temporal predictions mechanisms in probabilistic contexts and discusses their potential impact in auditory language processing
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Zhang, Jun. "Nearest neighbor queries in spatial and spatio-temporal databases /." View abstract or full-text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20ZHANG.
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Franco, Villoria Maria. "Temporal and spatial modelling of extreme river flow values in Scotland." Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/4017/.
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Extreme river flows can lead to inundation of floodplains, with consequent impacts for society, the environment and the economy. Flood risk estimates rely on river flow records, hence a good understanding of the patterns in river flow, and, in particular, in extreme river flow, is important to improve estimation of risk. In Scotland, a number of studies suggest a West to East rainfall gradient and increased variability in rainfall and river flow. This thesis presents and develops a number of statistical methods for analysis of different aspects of extreme river flows, namely the variability, temporal trend, seasonality and spatial dependence. The methods are applied to a large data set, provided by SEPA, of daily river flow records from 119 gauging stations across Scotland. The records range in length from 10 up to 80 years and are characterized by non-stationarity and long-range dependence. Examination of non-stationarity is done using wavelets. The results revealed significant changes in the variability of the seasonal pattern over the last 40 years, with periods of high and low variability associated with flood-rich and flood-poor periods respectively. Results from a wavelet coherency analysis suggest significant influence of large scale climatic indices (NAO, AMO) on river flow. A quantile regression model is then developed based on an additive regression framework using P-splines, where the parameters are fitted via weighted least squares. The proposed model includes a trend and seasonal component, estimated using the back-fitting algorithm. Incorporation of covariates and extension to higher dimension data sets is straightforward. The model is applied to a set of eight Scottish rivers to estimate the trend and seasonality in the 95th quantile of river flow. The results suggest differences in the long term trend between the East and the West and a more variable seasonal pattern in the East. Two different approaches are then considered for modelling spatial extremes. The first approach consists of a conditional probability model and concentrates on small subsets of rivers. Then a spatial quantile regression model is developed, extending the temporal quantile model above to estimate a spatial surface using the tensor product of the marginal B-spline bases. Residual spatial correlation using a Gaussian correlation function is incorporated into standard error estimation. Results from the 95th quantile fitted for individual months suggest changes in the spatial pattern of extreme river flow over time. The extension of the spatial quantile model to build a fully spatio-temporal model is briefly outlined and the main statistical issues identified.
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Gallacher, Kelly Marie. "Using river network structure to improve estimation of common temporal patterns." Thesis, University of Glasgow, 2016. http://theses.gla.ac.uk/7208/.
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Statistical models for data collected over space are widely available and commonly used. These models however usually assume relationships between observations depend on Euclidean distance between monitoring sites whose location is determined using two dimensional coordinates, and that relationships are not direction dependent. One example where these assumptions fail is when data are collected on river networks. In this situation, the location of monitoring sites along a river network relative to other sites is as important as the location in two dimensional space since it can be expected that spatial patterns will depend on the direction of water flow and distance between monitoring sites measured along the river network. Euclidean distance therefore might no longer be the most appropriate distance metric to consider. This is further complicated where it might be necessary to consider both Euclidean distance and distance along the river network if the observed variable is influenced by the land in which the river network is embedded. The Environment Agency (EA), established in 1996, is the government agency responsible for monitoring and improving the water quality in rivers situated in England (and Wales until 2013). A key responsibility of the EA is to ensure that efforts are made to improve and maintain water quality standards in compliance with EU regulations such as the Water Framework Directive (WFD, European Parliament (2000)) and Nitrates Directive (European Parliament, 1991). Environmental monitoring is costly and in many regions of the world funding for environmental monitoring is decreasing (Ferreyra et al., 2002). It is therefore important to develop statistical methods that can extract as much information as possible from existing or reduced monitoring networks. One way to do this is to identify common temporal patterns shared by many monitoring sites so that redundancy in the monitoring network could be reduced by removing non-informative sites exhibiting the same temporal patterns. In the case of river water quality, information about the shape of the river network, such as flow direction and connectivity of monitoring sites, could be incorporated into statistical techniques to improve statistical power and provide efficient inference without the increased cost of collecting more data. Reducing the volume of data required to estimate temporal trends would improve efficiency and provide cost savings to regulatory agencies. The overall aim of this thesis is to investigate how information about the spatial structure of river networks can be used to augment and improve the specfic trends obtained when using a variety of statistical techniques to estimate temporal trends in water quality data. Novel studies are designed to investigate the effect of accounting for river network structure within existing statistical techniques and, where necessary, statistical methodology is developed to show how this might be achieved. Chapter 1 provides an introduction to water quality monitoring and a description of several statistical methods that might be used for this. A discussion of statistical problems commonly encountered when modelling spatiotemporal data is also included. Following this, Chapter 2 applies a dimension reduction technique to investigate temporal trends and seasonal patterns shared among catchment areas in England and Wales. A novel comparison method is also developed to identify differences in the shape of temporal trends and seasonal patterns estimated using several different statistical methods, each of which incorporate spatial information in different ways. None of the statistical methods compared in Chapter 2 specifically account for features of spatial structure found in river networks: direction of water flow, relative influence of upstream monitoring sites on downstream sites, and stream distance. Chapter 3 therefore provides a detailed investigation and comparison of spatial covariance models that can be used to model spatial relationships found in river networks to standard spatial covariance models. Further investigation of the spatial covariance function is presented in Chapter 4 where a simulation study is used to assess how predictions from statistical models based on river network spatial covariance functions are affected by reducing the size of the monitoring network. A study is also developed to compare the predictive performance of statistical models based on a river network spatial covariance function to models based on spatial covariate information, but assuming spatial independence of monitoring sites. Chapters 3 and 4 therefore address the aim of assessing the improvement in information extracted from statistical models after the inclusion of information about river network structure. Following this, Chapter 5 combines the ideas of Chapters 2, 3 and 4 and proposes a novel statistical method where estimated common temporal patterns are adjusted for known spatial structure, identified in Chapters 3 and 4. Adjusting for known structure in the data means that spatial and temporal patterns independent of the river network structure can be more clearly identified since they are no longer confounded with known structure. The final chapter of this thesis provides a summary of the statistical methods investigated and developed within this thesis, identifies some limitations of the work carried out and suggests opportunities for future research. An Appendix provides details of many of the data processing steps required to obtain information about the river network structure in an appropriate form.
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McLean, Marnie Isla. "Spatio-temporal models for the analysis and optimisation of groundwater quality monitoring networks." Thesis, University of Glasgow, 2018. http://theses.gla.ac.uk/38975/.
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Commonly groundwater quality data are modelled using temporally independent spatial models. However, primarily due to cost constraints, data of this type can be sparse resulting in some sampling events only recording a few observations. With data of this nature, spatial models struggle to capture the true underlying state of the groundwater and building models with such small spatial datasets can result in unreliable predictions. This highlights the need for spatio-temporal models which `borrow strength' from earlier sampling events and which allow interpolations of groundwater concentrations between sampling points. To compare the relative merits of analysing groundwater quality data using spatial compared to spatio-temporal statistical models, a comparison study is presented using data from a hypothetical contaminant plume along with a real life dataset. In this study, the estimation accuracy of spatial p-spline and Kriging models are compared with spatio-temporal p-spline models. The results show that spatio-temporal methods can increase prediction efficiency markedly so that, in comparison with repeated spatial analysis, spatio-temporal methods can achieve the same level of performance but with smaller sample sizes. For the comparison study, in the spatio-temporal p-splines model, differing levels of variability over space and time were controlled using different numbers of basis functions rather than separate smoothing parameters due to the computational expense of their optimisation. However, deciding on the number of basis functions for each dimension is subjective due to space and time being measured on different scales, and thus methodology is developed to efficiently tune two smoothing parameters. The proposed methodology exploits lower resolution models to determine starting points for the optimisation procedure allowing for each parameter to be tuned separately. Working with spatio-temporal models can, however, pose their own problems. Due to the sporadic layout of many monitoring well networks, due to built-up urban areas and transport infrastructure, ballooning can be experienced in the predictions of these models. `Ballooning' is a term used to describe the event where high or low predictions are made in regions with little data support. To determine when this has occurred a measure is developed to highlight when ballooning may be present in the models predictions. In addition to the measure, to try to eliminate ballooning from happening in the first place, a penalty based on the idea that the total contaminant mass should not change significantly over time is proposed. However, the preliminary results presented here indicate that further work is needed to make this effective. It is shown that by adopting a spatio-temporal modelling framework a smoother, clearer and more accurate prediction through time can be achieved, compared to spatial modelling of individual time steps, whilst using fewer samples. This was shown using existing sampling schemes where the choice of sampling locations was made by someone with little knowledge or experience in sampling design. Sampling designs on fixed monitoring well networks are then explored and optimised through the minimisation two objective functions; the variance of the predicted plume mass (VM) and the integrated prediction variance (IV). Sampling design optimisations, using spatial and spatio-temporal p-spline models, are carried out, using a variety of numbers of wells and at various future sampling time points. The effects of well-specific sampling frequency are also investigated and it is found that both objective functions tend to propose wells for the next sampling design which have not been sampled recently. Often, an existing monitoring well network will need to be changed, either by adding new wells or by down-scaling and removing wells. The decision to add wells to the network comes at a financial expense, so it is of paramount importance that wells are added into areas where the gain in knowledge of the region is maximised. The decision to remove a well from the network is equally important and involves a trade-off between costs saved and information lost. The design objective functions suggest a well should be added in an area where the distance to the nearest neighbouring wells is greatest. Finally, consideration is given to optimal sampling designs when it is assumed the recorded data has multiplicative error - a common assumption in groundwater quality data. When modelling with this type of data, the response is normally log transformed prior to modelling and the predictions are then transformed back onto the original scale for interpretation. Assuming a log transformed response, the objective functions, initially presented, can be used if computation of the objective function is also on the log scale. However, if the desired scale of interpretation of the objective functions is the original scale but modelling was performed on the log scale, the resulting objective function values cannot simply be exponentiated to give an interpretation on the original scale. Modelling on the log scale while interpreting the objective function on the original scale can be achieved by adopting a lognormal distribution for the predicted response and subsequently numerically integrating its variance to compute the IV objective function. The results indicate that the designs do differ depending on which scale interpretation of the objective function is to be made. When interpreting on the original scale the objective function favours sampling from wells where higher values were previously estimated. Unfortunately, computation of the VM objective function when assuming a lognormal distribution has not been achieved so far.
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Pacella, Massimo. "High-dimensional statistics for complex data." Doctoral thesis, Universita degli studi di Salerno, 2018. http://hdl.handle.net/10556/3016.
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2016 - 2017High dimensional data analysis has become a popular research topic in the recent years, due to the emergence of various new applications in several fields of sciences underscoring the need for analysing massive data sets. One of the main challenge in analysing high dimensional data regards the interpretability of estimated models as well as the computational efficiency of procedures adopted. Such a purpose can be achieved through the identification of relevant variables that really affect the phenomenon of interest, so that effective models can be subsequently constructed and applied to solve practical problems. The first two chapters of the thesis are devoted in studying high dimensional statistics for variable selection. We firstly introduce a short but exhaustive review on the main developed techniques for the general problem of variable selection using nonparametric statistics. Lastly in chapter 3 we will present our proposal regarding a feature screening approach for non additive models developed by using of conditional information in the estimation procedure... [edited by Author]
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Ross, Beth E. "Assessing Changes in the Abundance of the Continental Population of Scaup Using a Hierarchical Spatio-Temporal Model." DigitalCommons@USU, 2012. http://digitalcommons.usu.edu/etd/1147.
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In ecological studies, the goal is often to describe and gain further insight into ecological processes underlying the data collected during observational studies. Because of the nature of observational data, it can often be difficult to separate the variation in the data from the underlying process or `state dynamics.' In order to better address this issue, it is becoming increasingly common for researchers to use hierarchical models. Hierarchical spatial, temporal, and spatio-temporal models allow for the simultaneous modeling of both first and second order processes, thus accounting for underlying autocorrelation in the system while still providing insight into overall spatial and temporal pattern. In this particular study, I use two species of interest, the lesser and greater scaup (Aythya affnis and Aythya marila), as an example of how hierarchical models can be utilized in wildlife management studies. Scaup are the most abundant and widespread diving duck in North America, and are important game species. Since 1978, the continental population of scaup has declined to levels that are 16% below the 1955-2010 average and 34% below the North American Waterfowl Management Plan goal. The greatest decline in abundance of scaup appears to be occurring in the western boreal forest, where populations may have depressed rates of reproductive success, survival, or both. In order to better understand the causes of the decline, and better understand the biology of scaup in general, a level of high importance has been placed on retrospective analyses that determine the spatial and temporal changes in population abundance. In order to implement Bayesian hierarchical models, I used a method called Integrated Nested Laplace Approximation (INLA) to approximate the posterior marginal distribution of the parameters of interest, rather than the more common Markov Chain Monte Carlo (MCMC) approach. Based on preliminary analysis, the data appeared to be overdispersed, containing a disproportionately high number of zeros along with a high variance relative to the mean. Thus, I considered two potential data models, the negative binomial and the zero-inflated negative binomial. Of these models, the zero-inflated negative binomial had the lowest DIC, thus inference was based on this model. Results from this model indicated that a large proportion of the strata were not decreasing (i.e., the estimated slope of the parameter was not significantly different from zero). However, there were important exceptions with strata in the northwest boreal forest and southern prairie parkland habitats. Several strata in the boreal forest habitat had negative slope estimates, indicating a decrease in breeding pairs, while some of the strata in the prairie parkland habitat had positive slope estimates, indicating an increase in this region. Additionally, from looking at plots of individual strata, it seems that the strata experiencing increases in breeding pairs are experiencing dramatic increases. Overall, my results support previous work indicating a decline in population abundance in the northern boreal forest of Canada, and additionally indicate that the population of scaup has increased rapidly in the prairie pothole region since 1957. Yet, by accounting for spatial and temporal autocorrelation in the data, it appears that declines in abundance are not as widespread as previously reported.
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Kobakian, Stephanie Rose. "New algorithms for effectively visualising Australian spatio-temporal disease data." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/203908/1/Stephanie_Kobakian_Thesis.pdf.
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This thesis contributes to improvements in effectively communicating population related cancer distributions and the associated burden of cancer on Australian communities. This thesis presents a new algorithm for creating an alternative map displays of tessellating hexagons. Alternative map displays can emphasise statistics in countries that contain densely populated cities. It is accompanied by a software implementation that automates the choice of one hexagon to represent each geographic unit, ensuring the statistic for each is equitably presented. The case study comparing a traditional choropleth map to the alternative hexagon tile map contributes to a growing field of visual inference studies.
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Tao, Yufei. "Indexing and query processing of spatio-temporal data /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?COMP%202002%20TAO.
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Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2002.Includes bibliographical references (leaves 208-215). Also available in electronic version. Access restricted to campus users.
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Goerg, Georg Matthias. "Learning Spatio-Temporal Dynamics: Nonparametric Methods for Optimal Forecasting and Automated Pattern Discovery." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/218.
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Many important scientific and data-driven problems involve quantities that vary over space and time. Examples include functional magnetic resonance imaging (fMRI), climate data, or experimental studies in physics, chemistry, and biology.
Principal goals of many methods in statistics, machine learning, and signal processing are to use this data and i) extract informative structures and remove noisy, uninformative parts; ii) understand and reconstruct underlying spatio-temporal dynamics that govern these systems; and iii) forecast the data, i.e., describe the system in the future.
Being data-driven problems, it is important to have methods and algorithms that work well in practice for a wide range of spatio-temporal processes as well as various data types. In this thesis I present such generally applicable statistical methods that address all three problems in a unifying manner.
I introduce two new techniques for optimal nonparametric forecasting of spatiotemporal data: hard and mixed LICORS (Light Cone Reconstruction of States). Hard LICORS is a consistent predictive state estimator and extends previous work from Shalizi (2003); Shalizi, Haslinger, Rouquier, Klinkner, and Moore (2006); Shalizi, Klinkner, and Haslinger (2004) to continuous-valued spatio-temporal fields. Mixed LICORS builds on a new, fully probabilistic model of light cones and predictive states mappings, and is an EM-like version of hard LICORS. Simulations show that it has much better finite sample properties than hard LICORS. I also propose a sparse variant of mixed LICORS, which improves out-of-sample forecasts even further.
Both methods can then be used to estimate local statistical complexity (LSC) (Shalizi, 2003), a fully automatic technique for pattern discovery in dynamical systems. Simulations and applications to fMRI data demonstrate that the proposed methods work well and give useful results in very general scientific settings.
Lastly, I made most methods publicly available as R (R Development Core Team, 2010) or Python (Van Rossum, 2003) packages, so researchers can use these methods and better understand, forecast, and discover patterns in the data they study.
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Ono, Sashi, and Hua Lee. "OBJECT RECOGNITION BY GROUND-PENETRATING RADAR IMAGING SYSTEMS WITH TEMPORAL SPECTRAL STATISTICS." International Foundation for Telemetering, 2004. http://hdl.handle.net/10150/604925.
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International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, CaliforniaThis paper describes a new approach to object recognition by using ground-penetrating radar (GPR) imaging systems. The recognition procedure utilizes the spectral content instead of the object shape in traditional methods. To produce the identification feature of an object, the most common spectral component is obtained by singular value decomposition (SVD) of the training sets. The identification process is then integrated into the backward propagation image reconstruction algorithm, which is implemented on the FMCW GPR imaging systems.
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Mohamad, Hamzah Firdaus. "Statistical analysis of freshwater parameters monitored at different temporal resolutions." Thesis, University of Glasgow, 2012. http://theses.gla.ac.uk/3350/.
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Nowadays, it is of great importance in ecological and environmental studies to investigate some prominent features in environmental determinants using appropriate statistical approaches. The initial motivation of this work was provided by the enthusiasm of the limnologist, biologist and statistician, interested in exploring and investigating certain features of time series data at different temporal resolutions to environmental parameters in freshwater. This thesis introduces a variety of statistical techniques which are used to provide sufficient information on the features of interest in the environmental variables in freshwater. Chapter 1 gives the background of the work, explores the details of the locations of the case studies, presents several statistical and ecological issues and outlines the aims and objectives of the thesis. Chapter 2 provides a review of some commonly used statistical modelling approaches to model trend and seasonality. All the modelling approaches are then applied to low temporal resolution (monthly data) of temperature and chlorophyll measurements from 1987-2005 for the north and south basins of Loch Lomond, Scotland. An investigation into the influence of temperature and nutrients on the variability of log chlorophyll is also carried out. Chapter 3 extends the modelling for temperature in Chapter 2 with the use of a mixed-effects model with different error structures for temperature data at a moderate temporal resolution (1 and 3 hourly data) in the north, mid and south basins. Three approaches are proposed to estimate the positions of a sharp change in gradient of temperature (thermocline) in deeper basins, using the maximum relative rate of change, changepoint regression and derivatives of a smooth curve. Chapter 4 investigates several features in semi-continuous environmental variables (15 and 30 minutes data). The temporal pattern of temperature, pH, conductivity and barometric pressure, and the evidence of similarity of the signals of pH and conductivity is determined, using wavelets. The time taken for pH and conductivity to return to `baseline levels' (recovery period) following extreme discharge is determined for different thresholds of `extreme discharge' for the Rivers Charr and Drumtee Burn, Scotland and models for the recovery period are proposed and fitted. Model validation is carried out for the River Charr and the occurrence of clusters of extreme discharge for both rivers is investigated using the extremal index. Chapter 5 summarises the main findings within this thesis and several potential areas for future work are suggested.
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Dou, Baojun. "Three essays on time series : spatio-temporal modelling, dimension reduction and change-point detection." Thesis, London School of Economics and Political Science (University of London), 2015. http://etheses.lse.ac.uk/3242/.
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Modelling high dimensional time series and non-stationary time series are two import aspects in time series analysis nowadays. The main objective of this thesis is to deal with these two problems. The first two parts deal with high dimensionality and the third part considers a change point detection problem. In the first part, we consider a class of spatio-temporal models which extend popular econometric spatial autoregressive panel data models by allowing the scalar coefficients for each location (or panel) different from each other. The model is of the following form: yt = D(λ0)Wyt + D(λ1)yt−1 + D(λ2)Wyt−1 + εt, (1) where yt = (y1,t, . . . , yp,t) T represents the observations from p locations at time t, D(λk) = diag(λk1, . . . , λkp) and λkj is the unknown coefficient parameter for the j-th location, and W is the p×p spatial weight matrix which measures the dependence among different locations. All the elements on the main diagonal of W are zero. It is a common practice in spatial econometrics to assume W known. For example, we may let wij = 1/(1 + dij ), for i ̸= j, where dij ≥ 0 is an appropriate distance between the i-th and the j-th location. It can simply be the geographical distance between the two locations or the distance reflecting the correlation or association between the variables at the two locations. In the above model, D(λ0) captures the pure spatial effect, D(λ1) captures the pure dynamic effect, and D(λ2) captures the time-lagged spatial effect. We also assume that the error term εt = (ε1,t, ε2,t, . . . , εp,t) T in (1) satisfies the condition Cov (yt−1, εt) = 0. When λk1 = · · · = λkp for all k = 1, 2, 3, (1) reduces to the model of Yu et al. (2008), in which there are only 3 unknown regressive coefficient parameters. In general the regression function in (1) contains 3p unknown parameters. To overcome the innate endogeneity, we propose a generalized Yule-Walker estimation method which applies the least squares estimation to a Yule-Walker equation. The asymptotic theory is developed under the setting that both the sample size and the number of locations (or panels) tend to infinity under a general setting for stationary and α-mixing processes, which includes spatial autoregressive panel data models driven by i.i.d. innovations as special cases. The proposed methods are illustrated using both simulated and real data. In part 2, we consider a multivariate time series model which decomposes a vector process into a latent factor process and a white noise process. Let yt = (y1,t, · · · , yp,t) T be an observable p × 1 vector time series process. The factor model decomposes yt in the following form: yt = Axt + εt , (2) where xt = (x1,t, · · · , xr,t) T is a r × 1 latent factor time series with unknown r ≤ p and A = (a1, a2, · · · , ar) is a p × r unknown constant matrix. εt is a white noise process with mean 0 and covariance matrix Σε. The first part of (2) is a dynamic part and the serial dependence of yt is driven by xt. We will achieve dimension reduction once r ≪ p in the sense that the dynamics of yt is driven by a much lower dimensional process xt. Motivated by practical needs and the characteristic of high dimensional data, the sparsity assumption on factor loading matrix is imposed. Different from Lam, Yao and Bathia (2011)’s method, which is equivalent to an eigenanalysis of a non negative definite matrix, we add a constraint to control the number of nonzero elements in each column of the factor loading matrix. Our proposed sparse estimator is then the solution of a constrained optimization problem. The asymptotic theory is developed under the setting that both the sample size and the dimensionality tend to infinity. When the common factor is weak in the sense that δ > 1/2 in Lam, Yao and Bathia (2011)’s paper, the new sparse estimator may have a faster convergence rate. Numerically, we employ the generalized deflation method (Mackey (2009)) and the GSLDA method (Moghaddam et al. (2006)) to approximate the estimator. The tuning parameter is chosen by cross validation. The proposed method is illustrated with both simulated and real data examples. The third part is a change point detection problem. we consider the following covariance structural break detection problem: Cov(yt)I(tj−1 ≤ t < tj ) = Σtj−1, j = 1, · · · , m + 1, where yt is a p × 1 vector time series, Σtj−1̸ = Σtj and {t1, . . ., tm} are change points, 1 = t0 < t1 < · · · < tm+1 = n. In the literature, the number of change points m is usually assumed to be known and small, because a large m would involve a huge amount of computational burden for parameters estimation. By reformulating the problem in a variable selection context, the group least absolute shrinkage and selection operator (LASSO) is proposed to estimate m and the locations of the change points {t1, . . ., tm}. Our method is model free, it can be extensively applied to multivariate time series, such as GARCH and stochastic volatility models. It is shown that both m and the locations of the change points {t1, . . . , tm} can be consistently estimated from the data, and the computation can be efficiently performed. An improved practical version that incorporates group LASSO and the stepwise regression variable selection technique are discussed. Simulation studies are conducted to assess the finite sample performance.
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Boushell, Audrey. "Comparing Generic Descriptive Analysis and Temporal Dominance of Sensations of Milk and Dark Chocolates and Effect of Training in Temporal Dominance of Sensations of Chocolates." Thesis, University of California, Davis, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1585048.
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Temporal Dominance of Sensations (TDS) is a sensory analysis method that measures the order and time that few key attributes are dominant throughout consumption of a product. Dominant attributes are those that catch the attention at a given moment, and are not necessarily related to intensity. A panel of 15 judges was trained first in Generic Descriptive Analysis (GDA) and then in TDS. This panel assessed 8 Guittard chocolates varying in amounts of cocoa solids, sugar, and fat. Both methods produced similar results. Samples were predominantly separated as milk chocolates and non-milk chocolates. Non-milk chocolates were sorted by attributes associated with cocoa and sugar content. The TDS data complemented the GDA data by providing additional information on how key attributes changed over time. A group of 98 untrained consumers then performed the same TDS procedure with the same chocolate samples. Both groups produced similar results for sample separation and sorting, but panelist data was superior. Panelists were better able to capture sensory changes over time and had more accurate and consistent understanding of certain attributes.
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Ma, Pulong. "Hierarchical Additive Spatial and Spatio-Temporal Process Models for Massive Datasets." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535635193581096.
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Shou, Yutao Sindy, and 壽玉濤. "Efficient query processing for spatial and temporal databases." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B29853655.
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Williamson, Laura. "Spatio-temporal variation in harbour porpoise distribution and activity." Thesis, University of Aberdeen, 2018. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=239337.
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Harbour porpoise (Phocoena phocoena) are the most abundant cetacean in UK waters, and are likely to be affected by a variety of marine industries and activities. This research uses data collected by acoustic recorders (C-PODs) and aerial video surveys to investigate patterns in porpoise detection. The findings can be split into five key themes, and are used to support the development of spatial management and survey recommendations. 1. Porpoise detection changes based on time of day in different habitats, indicating possible differences in diel habitat use and highlighting potential issues with visual or video data collection methods for assessing distribution. 2. Porpoise exhibit seasonal shifts in detection, yet year-round data are often lacking, therefore seasonal changes in distribution are often unknown. 3. The highest proportions of buzzes (associated with foraging) are not detected in areas with the highest relative density of porpoise. I propose that porpoise use different foraging strategies in different habitats which are not equally detectable by acoustic recorders. 4. Porpoise distribution may be influenced by the distribution of perceived risk from predator / competitor species (dolphins). Temporal partitioning of sites may arise either from porpoise actively avoiding times when bottlenose dolphins are expected to be present, or from porpoise and bottlenose preferences for different environmental conditions. 5. The choice of spatial modelling method can influence the fine-scale predictions of areas with the highest density. Improving our understanding of top and mesopredator ecology is informative for management strategies. Each of the points raised above should be considered when determining management strategies to minimise the impact from fisheries, offshore developments and other industrial activities on harbour porpoise.
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Garrett, Robert P. "Temporal and spatial distributions of Arctic sea ice thickness and pressure ridging statistics." Thesis, Monterey, California. Naval Postgraduate School, 1985. http://hdl.handle.net/10945/21582.
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Brynjarsdóttir, Jenný. "Dimension Reduced Modeling of Spatio-Temporal Processes with Applications to Statistical Downscaling." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1312935520.
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Iacopini, Matteo. "Essays on econometric modelling of temporal networks." Thesis, Paris 1, 2018. http://www.theses.fr/2018PA01E058/document.
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La théorie des graphes a longtemps été étudiée en mathématiques et en probabilité en tant qu’outil pour décrire la dépendance entre les nœuds. Cependant, ce n’est que récemment qu’elle a été mise en œuvre sur des données, donnant naissance à l’analyse statistique des réseaux réels.La topologie des réseaux économiques et financiers est remarquablement complexe: elle n’est généralement pas observée, et elle nécessite ainsi des procédures inférentielles adéquates pour son estimation, d’ailleurs non seulement les nœuds, mais la structure de la dépendance elle-même évolue dans le temps. Des outils statistiques et économétriques pour modéliser la dynamique de changement de la structure du réseau font défaut, malgré leurs besoins croissants dans plusieurs domaines de recherche. En même temps, avec le début de l’ère des “Big data”, la taille des ensembles de données disponibles devient de plus en plus élevée et leur structure interne devient de plus en plus complexe, entravant les processus inférentiels traditionnels dans plusieurs cas. Cette thèse a pour but de contribuer à ce nouveau champ littéraire qui associe probabilités, économie, physique et sociologie en proposant de nouvelles méthodologies statistiques et économétriques pour l’étude de l’évolution temporelle des structures en réseau de moyenne et haute dimensionGraph theory has long been studied in mathematics and probability as a tool for describing dependence between nodes. However, only recently it has been implemented on data, giving birth to the statistical analysis of real networks.The topology of economic and financial networks is remarkably complex: it is generally unobserved, thus requiring adequate inferential procedures for it estimation, moreover not only the nodes, but the structure of dependence itself evolves over time. Statistical and econometric tools for modelling the dynamics of change of the network structure are lacking, despite their increasing requirement in several fields of research. At the same time, with the beginning of the era of “Big data” the size of available datasets is becoming increasingly high and their internal structure is growing in complexity, hampering traditional inferential processes in multiple cases.This thesis aims at contributing to this newborn field of literature which joins probability, economics, physics and sociology by proposing novel statistical and econometric methodologies for the study of the temporal evolution of network structures of medium-high dimension
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Oberer, Richard B. "Fission multiplicity detection with temporal gamma-neutron discrimination from higher-order time correlation statistics." Diss., Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/17632.
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Li, Xintong. "Modeling for Spatial and Spatio-Temporal Data with Applications." Diss., Kansas State University, 2018. http://hdl.handle.net/2097/38749.
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Doctor of PhilosophyDepartment of Statistics
Juan Du
It is common to assume the spatial or spatio-temporal data are realizations of underlying random elds or stochastic processes. E ective approaches to modelling of the underlying autocorrelation structure of the same random eld and the association among multiple processes are of great demand in many areas including atmospheric sciences, meteorology and agriculture. To this end, this dissertation studies methods and application of the spatial modeling of large-scale dependence structure and spatio-temporal regression modelling. First, variogram and variogram matrix functions play important roles in modeling dependence structure among processes at di erent locations in spatial statistics. With more and more data collected on a global scale in environmental science, geophysics, and related elds, we focus on the characterizations of the variogram models on spheres of all dimensions for both stationary and intrinsic stationary, univariate and multivariate random elds. Some e cient approaches are proposed to construct a variety of variograms including simple polynomial structures. In particular, the series representation and spherical behavior of intrinsic stationary random elds are explored in both theoretical and simulation study. The applications of the proposed model and related theoretical results are demonstrated using simulation and real data analysis. Second, knowledge of the influential factors on the number of days suitable for fieldwork (DSFW) has important implications on timing of agricultural eld operations, machinery decision, and risk management. To assess how some global climate phenomena such as El Nino Southern Oscillation (ENSO) a ects DSFW and capture their complex associations in space and time, we propose various spatio-temporal dynamic models under hierarchical Bayesian framework. The Integrated Nested Laplace Approximation (INLA) is used and adapted to reduce the computational burden experienced when a large number of geo-locations and time points is considered in the data set. A comparison study between dynamics models with INLA viewing spatial domain as discrete and continuous is conducted and their pros and cons are evaluated based on multiple criteria. Finally a model with time- varying coefficients is shown to reflect the dynamic nature of the impact and lagged effect of ENSO on DSFW in US with spatio-temporal correlations accounted.
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Partsinevelos, Panayotis. "Detection and Generalization of Spatio-temporal Trajectories for Motion Imagery." Fogler Library, University of Maine, 2002. http://www.library.umaine.edu/theses/pdf/PartsinevelosP2002.pdf.
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Sariaslan, Nazli. "The Effect Of Temporal Aggregation On Univariate Time Series Analysis." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612528/index.pdf.
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Most of the time series are constructed by some kind of aggregation and temporal
aggregation that can be defined as aggregation over consecutive time periods.
Temporal aggregation takes an important role in time series analysis since the choice
of time unit clearly influences the type of model and forecast results. A totally
different time series model can be fitted on the same variable over different time
periods. In this thesis, the effect of temporal aggregation on univariate time series
models is studied by considering modeling and forecasting procedure via a
simulation study and an application based on a southern oscillation data set.
Simulation study shows how the model, mean square forecast error and estimated
parameters change when temporally aggregated data is used for different orders of
aggregation and sample sizes. Furthermore, the effect of temporal aggregation is also
demonstrated through southern oscillation data set for different orders of
aggregation. It is observed that the effect of temporal aggregation should be taken
into account for data analysis since temporal aggregation can give rise to misleading
results and inferences.
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Brulé, Thibault. "Spectral and temporal distribution of biomolecules by Dynamic SERS." Thesis, Dijon, 2014. http://www.theses.fr/2014DIJOS037/document.
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Dans cette thèse, la définition du SERS en tant que biocapteur a été testée et une nouvelle approche a été développée. Ainsi, concernant la quantification, il est montré que le SERS peut-être un outil très efficace. Concernant la sélectivité, la qualité spectrale a été améliorée. Une excellente limite de détection associée à l’approche statistique et dynamique permet une très bonne sensibilité (inférieure au nanomolaire). Cette approche permet également une grande reproductibilité du capteur dans le temps. Ainsi, alors que le SERS ne réponds pas forcément bien aux caractéristiques d’un capteur dans son approche classique, dans notre cas le couplage entre un substrat de nanoparticules d’or non fonctionnalisées associé à un système microfluidique, le tout monté sur un microscope confocal pour des études temporelles dynamiques analysées statistiquement a contribué à définir le SERS comme un biocapteur efficaceIn this thesis, the definition of SERS as a biosensor has been tested and a new approach developed for. Also, in terms of quantification, it has been shown that SERS can be an efficient tool. Concerning the selectivity, the spectral quality was improved. A low limit of detection associated to the statistical and dynamic approach allows a very good sensitivity (under the nanomolar). This approach also enables a high reproducibility in time of the sensor. Thus, as low as SERS does not well answer to the sensor capabilities in a classical approach, in our case the coupling between a non-functionalized GNPs substrate coupled with a microfluidic chip, all mounted on a confocal microscope for temporal dynamic studies statistically analyzed has contributed to define SERS as an efficient biosensor
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Agarwal, Abhijat. "A New Approach to Spatio-Temporal Kriging and Its Applications." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1306871646.
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Tanadini, Matteo. "Incorporating spatial and temporal variability in analyses of the relationship between biodiversity and ecosystem functioning." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:73c52d36-2e8a-4e04-92e0-a67ed93d7090.
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In the last few decades, a growing literature has examined how biodiversity influences ecosystem functioning. This body of work has greatly improved our understanding of ecosystem functioning and its modulation by biodiversity. In particular, there is nowadays large consensus that biodiversity increases ecosystem productivity, and stabilises ecosystems. Early investigations were largely theoretical or involved simple experiments run in laboratory conditions, but over time biodiversity ecosystem-functioning experiments evolved to more realistic field experiments that better represent the real conditions found in natural ecosystems. In particular, these experiments are often run on larger spatial scales and over longer time frames allowing for the effect of environmental heterogeneity and temporal fluctuations to be explored. The designs of these experiments evolved along with the questions addressed in this field of research. However, the analytical tools used in the analyses of these experiments followed a slightly different path. In particular, most of the metrics currently used to analyse biodiversity ecosystem functioning experiments are not entirely suited to properly deal with the complexity of modern designs as they make a number of assumptions that are not met any more. In my thesis I developed a unified framework, based on the tailored use of Linear Mixed Effects Models, to analyse biodiversity-ecosystem functioning experiments such that the new complexities of these experiments can be taken into account. This thesis aimed to bring the focus of the analysis back to the biological interpretation of the results. I successfully applied my approach to several data sets. The framework developed here is expected to improve greatly our understanding of ecosystem functioning and how biodiversity modulates it. It also sheds new light on past research in this field. The great flexibility of the new approach makes it possible to let these experiments to evolve such that new biological questions can be addressed.
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Soale, Abdul-Nasah. "Spatio-Temporal Analysis of Point Patterns." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etd/3120.
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In this thesis, the basic tools of spatial statistics and time series analysis are applied to the case study of the earthquakes in a certain geographical region and time frame. Then some of the existing methods for joint analysis of time and space are described and applied. Finally, additional research questions about the spatial-temporal distribution of the earthquakes are posed and explored using statistical plots and models. The focus in the last section is in the relationship between number of events per year and maximum magnitude and its effect on how clustered the spatial distribution is and the relationship between distances in time and space in between consecutive events as well as the distribution of the distances.
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Chen, Linchao. "Predictive Modeling of Spatio-Temporal Datasets in High Dimensions." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429586479.
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Butler, André J. "Temporal and spatial analysis of PM₂₅ mass and composition in Atlanta." Thesis, Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/24143.
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Pfundstein, Maximilian. "Human Age Prediction Based on Real and Simulated RR Intervals using Temporal Convolutional Neural Networks and Gaussian Processes." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165987.
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Electrocardiography (ECG) is a non-invasive method used in medicine to track the electrical pulses sent by the heart. The time between two subsequent electrical impulses and hence the heartbeat of a subject, is referred to as an RR interval. Previous studies show that RR intervals can be used for identifying sleep patterns and cardiovascular diseases. Additional research indicates that RR intervals can be used to predict the cardiovascular age of a subject. This thesis investigates, if this assumption is true, based on two different datasets as well as simulated data based on Gaussian Processes. The datasets used are Holter recordings provided by the University of Gdańsk as well as a dataset provided by Physionet. The former represents a balanced dataset of recordings during nocturnal sleep of healthy subjects whereas the latter one describes an imbalanced dataset of records of a whole day of subjects that suffered from myocardial infarction. Feature-based models as well as a deep learning architecture called DeepSleep, based on a paper for sleep stage detection, are trained. The results show, that the prediction of a subject's age, only based in RR intervals, is difficult. For the first dataset, the highest obtained test accuracy is 37.84 per cent, with a baseline of 18.23 per cent. For the second dataset, the highest obtained accuracy is 42.58 per cent with a baseline of 39.14 per cent. Furthermore, data is simulated by fitting Gaussian Processes to the first dataset and following a Bayesian approach by assuming a distribution for all hyperparameters of the kernel function in use. The distributions for the hyperparameters are continuously updated by fitting a Gaussian Process to a slices of around 2.5 minutes. Then, samples from the fitted Gaussian Process are taken as simulated data, handling impurity and padding. The results show that the highest accuracy achieved is 31.12 per cent with a baseline of 18.23 per cent. Concludingly, cardiovascular age prediction based on RR intervals is a difficult problem and complex handling of impurity does not necessarily improve the results.
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Parsons, Blair. "Malleefowl in the fragmented Western Australian wheatbelt : spatial and temporal analysis of a threatened species." University of Western Australia. School of Animal Biology, 2009. http://theses.library.uwa.edu.au/adt-WU2009.0050.
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[Truncated abstract] The malleefowl (Leipoa ocellata) is a large, ground-dwelling bird that is listed as threatened in all states of Australia in which it occurs. Its range encompasses much of southern Australia; however, much of it has been cleared for agriculture. Malleefowl are thought to have suffered substantial decline owing to multiple threats that include habitat loss, predation from exotic predators, grazing of habitat by introduced herbivores and fire - common threats in the decline of many Australian vertebrate species. The malleefowl has an unmistakeable appearance, unique biology, and widespread distribution across Australia. Consequently, it has been the focus of much scientific and community interest. In the Western Australian wheatbelt, community groups are working to conserve the species and have been actively collecting data on its distribution for over 15 years. The vast majority of these data are presence-only and have been collected in an opportunistic manner but, combined with long-term data from government agencies and museums spanning over 150 years, they present a significant opportunity to inform ecological questions relevant to the conservation of the species. The purpose of this study was to answer key ecological questions regarding the distribution, status and habitat preferences of malleefowl using unstructured occurrence records supplemented by reliable absences derived from Bird Atlas data sets and targeted surveys. Malleefowl in the Western Australian wheatbelt were used as a case study to illustrate: 1) how the decline of a species can be quantified and causes of that decline identified; and 2) how threats can be identified and responses to threats explored. I used bioclimatic modelling to define and explore variation within the climatic niche of the Malleefowl across Australia. '...' This thesis provides substantial additional knowledge about the ecology, distribution and status of malleefowl in Western Australia. It also illustrates how opportunistic and unstructured data can be augmented to investigate key aspects of a species' ecology. Despite the limitations of these data, which primarily relate to variation in observer effort across time and space, they can provide important outcomes that may not be achieved using standard survey and data collection techniques. The utility of opportunistic data is greatest in situations where the species: is recognisable and easily observed; is relatively sedentary; and occurs within a landscape containing consistent land use and habitat types. The approaches used in this study could be applied by researchers to situations where community interest exists for species with these attributes. At a national scale, the malleefowl is predicted to decline by at least 20% over the next three generations. The findings of this thesis suggest that the future for the species in the Western Australian wheatbelt may not be as dire as predicted elsewhere within its range, owing largely to the easing and cessation of threatening processes (e.g. land clearing, grazing of habitat by livestock) and the ability of the species to occupy a variety of habitat types. Despite this perceived security, some caution must be exercised until there is a more complete knowledge of the impact of fox predation and reduced rainfall due to climate change on malleefowl populations. Furthermore, the status of the species beyond the agricultural landscapes in Western Australia requires closer examination.
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Magalhães, Gledson Bezerra. "Comportamento espaço-temporal da dengue e sua relação com os elementos atmosféricos e socioeconômicos em Fortaleza/CE." reponame:Repositório Institucional da UFC, 2015. http://www.repositorio.ufc.br/handle/riufc/17636.
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MAGALHÃES, Gledson Bezerra. Comportamento espaço-temporal da dengue e sua relação com os elementos atmosféricos e socioeconômicos em Fortaleza/CE. 2015. 265 f. Tese (Doutorado em geografia)- Universidade Federal do Ceará, Fortaleza-CE, 2015.Submitted by Elineudson Ribeiro (elineudsonr@gmail.com) on 2016-06-10T19:36:17Z No. of bitstreams: 1 2015_tese_gbmagalhaes.pdf: 9936872 bytes, checksum: 63f3082d7d6f61b0f4795c5dd1925ed8 (MD5)
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O objetivo deste trabalho constitui-se realizar uma análise sobre comportamento da dengue na cidade de Fortaleza-CE, levando em conta a influência das condições socioeconômicas e climáticas, e enfocando as diferenças socioespaciais que garantem a produção de um clima urbano e a manutenção da dengue na cidade. Para isso, foram adquiridos dados de variáveis atmosféricas, epidemiológicas e socioeconômicas. Elaborarou-se mapas, gráficos e cálculos geoestatísticos (Alfa de Crobach, Correlação de Pearson, autocorrelação espacial – I de Moran e LISA). Executou-se uma análise espaço temporal em uma perspectiva do geral ao particular e adentrou-se na análise episódica dos fenômenos epidemiológicos. A umidade relativa do ar foi a variável atmosférica que mais se correlacionou com os casos de dengue. Verificou-se correlações mais elevadas em até um mês de diferença entre as chuvas e o aumento de casos da doença, diminuindo com o adiantamento de 2 e 3 meses. Constataram-se correlações negativas entre os casos de dengue e a temperatura média do ar devido à influência da precipitação. Fortaleza apresenta espaços onde coexistem populações com precariedades sociais, vivendo em áreas frágeis ambientalmente e com elevada quantidade de casos de dengue. Os bairros da porção oeste, sudoeste e sudeste da cidade foram onde se iniciaram os episódios epidêmicos de 2011 e 2012, e também onde a doença se manteve endêmica no episódio de 2013. Nessas regiões a dengue se proliferou com maior rapidez logo no início das epidemias investigadas. As correlações entre as variáveis epidemiológicas e socioeconômicas são mais fortes nos meses de início das epidemias, quando a doença ainda não está totalmente disseminada pela cidade. As variáveis Renda Média de Moradores por Domicílio e Porcentagem de Domicílios Ligados à Rede Geral de Esgoto ou Pluvial foram as variáveis que mais se correlacionaram com a incidência da doença. A autocorrelação espacial aponta os bairros com piores condições de saneamento como aqueles com elevada incidência no início dos episódios epidêmicos. Nos episódios investigados a epidemia se anunciou com o aparecimento de alguns casos próximos entre si, para em seguida se configurar em uma epidemia explosiva.
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MagalhÃes, Gledson Bezerra. "Comportamento espaÃo-temporal da dengue e sua relaÃÃo com os elementos atmosfÃricos e socioeconÃmicos em Fortaleza/CE." Universidade Federal do CearÃ, 2015. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=13901.
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CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel SuperiorO objetivo deste trabalho constitui-se realizar uma anÃlise sobre comportamento da dengue na cidade de Fortaleza-CE, levando em conta a influÃncia das condiÃÃes socioeconÃmicas e climÃticas, e enfocando as diferenÃas socioespaciais que garantem a produÃÃo de um clima urbano e a manutenÃÃo da dengue na cidade. Para isso, foram adquiridos dados de variÃveis atmosfÃricas, epidemiolÃgicas e socioeconÃmicas. Elaborarou-se mapas, grÃficos e cÃlculos geoestatÃsticos (Alfa de Crobach, CorrelaÃÃo de Pearson, autocorrelaÃÃo espacial â I de Moran e LISA). Executou-se uma anÃlise espaÃo temporal em uma perspectiva do geral ao particular e adentrou-se na anÃlise episÃdica dos fenÃmenos epidemiolÃgicos. A umidade relativa do ar foi a variÃvel atmosfÃrica que mais se correlacionou com os casos de dengue. Verificou-se correlaÃÃes mais elevadas em atà um mÃs de diferenÃa entre as chuvas e o aumento de casos da doenÃa, diminuindo com o adiantamento de 2 e 3 meses. Constataram-se correlaÃÃes negativas entre os casos de dengue e a temperatura mÃdia do ar devido à influÃncia da precipitaÃÃo. Fortaleza apresenta espaÃos onde coexistem populaÃÃes com precariedades sociais, vivendo em Ãreas frÃgeis ambientalmente e com elevada quantidade de casos de dengue. Os bairros da porÃÃo oeste, sudoeste e sudeste da cidade foram onde se iniciaram os episÃdios epidÃmicos de 2011 e 2012, e tambÃm onde a doenÃa se manteve endÃmica no episÃdio de 2013. Nessas regiÃes a dengue se proliferou com maior rapidez logo no inÃcio das epidemias investigadas. As correlaÃÃes entre as variÃveis epidemiolÃgicas e socioeconÃmicas sÃo mais fortes nos meses de inÃcio das epidemias, quando a doenÃa ainda nÃo està totalmente disseminada pela cidade. As variÃveis Renda MÃdia de Moradores por DomicÃlio e Porcentagem de DomicÃlios Ligados à Rede Geral de Esgoto ou Pluvial foram as variÃveis que mais se correlacionaram com a incidÃncia da doenÃa. A autocorrelaÃÃo espacial aponta os bairros com piores condiÃÃes de saneamento como aqueles com elevada incidÃncia no inÃcio dos episÃdios epidÃmicos. Nos episÃdios investigados a epidemia se anunciou com o aparecimento de alguns casos prÃximos entre si, para em seguida se configurar em uma epidemia explosiva.
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Kang, Lei. "Reduced-Dimension Hierarchical Statistical Models for Spatial and Spatio-Temporal Data." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1259168805.
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Rodríguez, Cortés Francisco Javier. "Modelling, Estimation and Applications of Second-Order Spatio-Temporal Characteristics of Point Processes." Doctoral thesis, Universitat Jaume I, 2014. http://hdl.handle.net/10803/394025.
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Esta tesis se centra principalmente en el desarrollo de propiedades y características de los estimadores de segundo orden de procesos puntuales y espacio-temporales. En primer lugar, se presenta un marco teórico acerca de procesos puntuales espaciales y espacio-temporales. El resto de la tesis se organiza como sigue. En el capítulo 2, se presenta una nueva familia de kernel positivos y óptimos, además se propone un estimador insensgado alternativo para la función de la densidad del producto. Su rendimiento se compara para varios kernel mediante MISE. En el capítulo 3, se dada un nuevo estimador kernel de la función de la densidad producto espacio-temporal y también se desarrollan expresiones cerradas para la varianza en el caso de Poisson. En el Capítulo 4, nos centramos en los métodos de orientación de segundo orden los cuales proporcionan una herramienta para el análisis natural para los datos espaciales anisótropicos. Finalmente, se proporciona una descripción general de los proyectos de investigación actualmente en curso que han surgido motivadas por la estrecha relación con las propiedades de segundo orden de los procesos puntuales espaciales y espacio-temporales.This thesis is mainly focused on developing properties and estimators for second-order characteristics of spatio-temporal point processes. First, we present a theoretical framework of spatial and spatio-temporal point processes. The rest of the thesis is organized as follows. In Chapter 2 we present a new family of optimal and positive kernels an alternative unbiased estimator for the product density function. Its performance is compare under several kernel through MISE. In Chapter 3 a new kernel estimator of spatio-temporal product density function are given and also are developed close expressions for the variance under the Poisson case. En el capítulo 4 nos centramos en los métodos de orientación de segundo orden que proporcionan una herramienta natural para el análisis de los datos de proceso Punto espaciales anisótropas. Finally, we provide a general description of the currently ongoing research projects which have emerged motivated by the close relationship with the second-order properties.
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Thomas, Zachary Micah. "Bayesian Hierarchical Space-Time Clustering Methods." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1435324379.
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