Global ETD Search

1	Robust Prediction of Large Spatio-Temporal Datasets Chen, Yang 24 May 2013 (has links) This thesis describes a robust and efficient design of Student-t based Robust Spatio-Temporal Prediction, namely, St-RSTP, to provide estimation based on observations over spatio-temporal neighbors. It is crucial to many applications in geographical information systems, medical imaging, urban planning, economy study, and climate forecasting. The proposed St-RSTP is more resilient to outliers or other small departures from model assumptions than its ancestor, the Spatio-Temporal Random Effects (STRE) model. STRE is a statistical model with linear order complexity for processing large scale spatiotemporal data. However, STRE has been shown sensitive to outliers or anomaly observations. In our design, the St-RSTP model assumes that the measurement error follows Student's t-distribution, instead of a traditional Gaussian distribution. To handle the analytical intractable inference of Student's t model, we propose an approximate inference algorithm in the framework of Expectation Propagation (EP). Extensive experimental evaluations, based on both simulation and real-life data sets, demonstrated the robustness and the efficiency of our Student-t prediction model compared with the STRE model. / Master of Science Robust Prediction Expectation Propagation Student's t Model Bayesian Hierarchical Model Spatio-Temporal Process
2	Analysis of Binary Data via Spatial-Temporal Autologistic Regression Models Wang, Zilong 01 January 2012 (has links) Spatial-temporal autologistic models are useful models for binary data that are measured repeatedly over time on a spatial lattice. They can account for effects of potential covariates and spatial-temporal statistical dependence among the data. However, the traditional parametrization of spatial-temporal autologistic model presents difficulties in interpreting model parameters across varying levels of statistical dependence, where its non-negative autocovariates could bias the realizations toward 1. In order to achieve interpretable parameters, a centered spatial-temporal autologistic regression model has been developed. Two efficient statistical inference approaches, expectation-maximization pseudo-likelihood approach (EMPL) and Monte Carlo expectation-maximization likelihood approach (MCEML), have been proposed. Also, Bayesian inference is considered and studied. Moreover, the performance and efficiency of these three inference approaches across various sizes of sampling lattices and numbers of sampling time points through both simulation study and a real data example have been studied. In addition, We consider the imputation of missing values is for spatial-temporal autologistic regression models. Most existing imputation methods are not admissible to impute spatial-temporal missing values, because they can disrupt the inherent structure of the data and lead to a serious bias during the inference or computing efficient issue. Two imputation methods, iteration-KNN imputation and maximum entropy imputation, are proposed, both of them are relatively simple and can yield reasonable results. In summary, the main contributions of this dissertation are the development of a spatial-temporal autologistic regression model with centered parameterization, and proposal of EMPL, MCEML, and Bayesian inference to obtain the estimations of model parameters. Also, iteration-KNN and maximum entropy imputation methods have been presented for spatial-temporal missing data, which generate reliable imputed values with the reasonable efficient imputation time. Autologistic regression models Binary data Imputation Spatial-temporal process Biostatistics Statistical Models
3	Analysis of Spatial Data Zhang, Xiang 01 January 2013 (has links) In many areas of the agriculture, biological, physical and social sciences, spatial lattice data are becoming increasingly common. In addition, a large amount of lattice data shows not only visible spatial pattern but also temporal pattern (see, Zhu et al. 2005). An interesting problem is to develop a model to systematically model the relationship between the response variable and possible explanatory variable, while accounting for space and time effect simultaneously. Spatial-temporal linear model and the corresponding likelihood-based statistical inference are important tools for the analysis of spatial-temporal lattice data. We propose a general asymptotic framework for spatial-temporal linear models and investigate the property of maximum likelihood estimates under such framework. Mild regularity conditions on the spatial-temporal weight matrices will be put in order to derive the asymptotic properties (consistency and asymptotic normality) of maximum likelihood estimates. A simulation study is conducted to examine the finite-sample properties of the maximum likelihood estimates. For spatial data, aside from traditional likelihood-based method, a variety of literature has discussed Bayesian approach to estimate the correlation (auto-covariance function) among spatial data, especially Zheng et al. (2010) proposed a nonparametric Bayesian approach to estimate a spectral density. We will also discuss nonparametric Bayesian approach in analyzing spatial data. We will propose a general procedure for constructing a multivariate Feller prior and establish its theoretical property as a nonparametric prior. A blocked Gibbs sampling algorithm is also proposed for computation since the posterior distribution is analytically manageable. Autoregressive models Spatial-temporal process Multivariate Feller prior Blocked Gibbs sampling Statistical Methodology Statistical Models Statistical Theory
4	Modal Logic and the two-variable fragment: Revised Version Lutz, Carsten, Sattler, Ulrike, Wolter, Frank 24 May 2022 (has links) We introduce a modal language L which is obtained from standard modal logic by adding the Boolean operators on accessibility relations, the identity relation, and the converse of relations. It is proved that L has the same expressive power as the two-variable fragment FO² of first-order logic, but speaks less succinctly about relational structures: if the number of relations is bounded, then L-satisfiability is EXPTIME-complete but FO² satisfiability is NEXPTIME-complete. We indicate that the relation between L and FO² provides a general framework for comparing modal and temporal languages with first-order languages. info:eu-repo/classification/ddc/004 ddc:004
5	Expressing Temporality In Graphical User Interface Olcay, Taner January 2020 (has links) Temporality has been given attention in HCI research, with scholars arguing that temporal aspects in function-oriented graphical user interface are overlooked. However, these works have not adequately addressed practical approaches to manifest time in the design of such. This paper presents an approach for implementing temporal metaphors in the design of graphical user interface. In this design research, I materialize temporal metaphors into material qualities, in order to manifest time into the design of graphical user interface and shape the experiences of such designs. I argue that the design of temporal metaphors may express traces of time in graphical user interface differently from contemporary designs. I discuss implications and significance of unfolding experience over time. In conclusion, this design research, by articulating the experiences of its design works, sheds new light on the meanings of expressing temporal metaphors in the design of graphical user interface. interaction temporality graphical user interface gui hci material temporal metaphor experimentation research design auto-critique realism reductionism temporalism organic inorganic material thinking anatomical time continuous traces of time contemporary experience qualities function-oriented aesthetics experiential sensuous experience-oriented long-term materiality experiment criticism materialism natural living time realistic features temporal process Social Sciences Samhällsvetenskap

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