Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 24 for “"Conditional Autoregressive"”.
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Applying an Intrinsic Conditional Autoregressive Reference Prior for Areal Data
… common to spatial data. In particular, intrinsic conditional autoregressive (ICAR) models are commonly assigned as priors for spatial random effects in hierarchical models for areal data corresponding to spatial partitions of a region. However, selection of prior distributions for these spatial …
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Hierarchical Gaussian Processes for Spatially Dependent Model Selection
… estimation in nearby clusters. We utilize the Conditional Autoregressive (CAR) model and Ising distribution to provide intra-cluster correlation on the linear effects and model inclusion indicators, while modeling inter-cluster correlation with separate Gaussian processes. We apply our model …
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Bayesian analysis of spatial and survival models with applications of computation techniques
… or with right-censored failure time. A conditional autoregressive (CAR) prior is used for the model to capture spatial effects. Markov chain Monte Carlo (MCMC) methods are used in the sampling. The Ancillary-Sufficient Interweaving Strategy (ASIS) is applied to improve the performance …
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Bayesian spatio-temporal modelling, mapping and prediction of disease risk
… μέσω δομημένων τυχαίων επιδράσεων με χρήση Conditional Autoregressive (CAR) προσεγγίσεων, συμπεριλαμβανομένων των ICAR, Leroux και των μοντέλων Besag–York–Mollié (BYM και BYM2). Παράλληλα, εξετάζεται η συνεχής χωρική αναπαράσταση μέσω Γκαουσιανών Χωρικών Πεδίων με συναρτήσεις συσχέτισης …
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Data combining using mixtures of g-priors with application on county-level female breast cancer prevalence
… combined data. To resolve the newly identified conditional Lindley paradox and relax constraints on design matrix, data combining with independent mixtures of g-prior is explored, where a different scale is used for each group of coefficients. We not only perform a posterior variance analysis, …
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Bayesian Factor Models for Clustering and Spatiotemporal Analysis
… the factor loadings matrix is assigned intrinsic conditional autoregressive (ICAR) priors. Therefore, we call our approach the Dynamic ICAR Spatiotemporal Factor Models (DIFM). Our second model, Bayesian Clustering Factor Model (BCFM) assumes latent factors and clusters are present in the data. We …
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Diameter Distributions of Juvenile Stands of Loblolly Pine (Pinus taeda L.) with Different Planting Densities
… age was present, but leveled off by age 5. A conditional autoregressive model was utilized to evaluate the amount of spatial influence stems in a stand have on one another. The occurrence of significant spatial influences was positively associated with age through age 8, the trend then leveled …
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Spatial Data Analysis
… regression techniques, such as Simultaneous Autoregressive (SAR), Conditional Autoregressive (CAR), Generalized Least Squares (GLS), Linear Mixed Effects (LME), and Geographically Weighted Regression (GWR) were discussed. Comparative studies of these modeling techniques were carried out using …
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Advances in spatial modeling for complex data with applications to symbolic data and spatial transcriptomics.
… gene-level dependencies through pathway-informed Conditional Autoregressive (CAR) priors for the detection of Spatially Expressed (SE) genes in Spatial Transcriptomics data. All proposed models are evaluated through detailed simulation studies under realistic conditions to assess their reliability …
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Models and methods for computationally efficient analysis of large spatial and spatio-temporal data
… big n problem" are reviewed, and an extended autoregressive model, called the EAR model, is proposed as a parsimonious model that accounts for smoothness of a process collected over space. It is an extension of the Pettitt et a1. as well as Czado and Prokopenko parameterizations of the spatial …
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Range-based Volatility Modelling, Forecasting and Spillovers
… estimators, including the vanilla and asymmetric conditional autoregressive range(CARR) model, the simple, component and fractionally integrated range-based exponential generalised autoregressive conditional heteroscedasticity (REGARCH) models, and the cyclical model. I assess the performance of …
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Applications of quantile regression to estimation and detection of some tail characteristics
… algorithm to perform Bayesian inference on the Conditional Autoregressive Value at Risk (CAViaR) models proposed by Engle and Manganelli (2004) based on quantile regression. Using the S&P 500 index as an example, we show that the proposed Bayesian approach adds value to the original estimation …
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Efficient Bayesian analysis of spatial occupancy models
… model. We incorporate the widely used Intrinsic Conditional Autoregressive (ICAR) prior model to specify the spatial random effect in our sampler. We also develop OccuSpytial, a statistical package implementing our Gibbs sampler in the Python programming language. The aim of this study is to …
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Shared-component model with application to mapping gender specific pattern in HIV testing and condom use in Namibia
… priors were assumed for the fixed effects, while conditional autoregressive priors were assigned to the structured spatial effects and exchangeable priors for the unstructured random effects. Simulation techniques through Markov Chain Monte Carlo were applied for model estimation. Common and …
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Statistical methods for genetics and genomics studies
… Bayesian statistical models with a Gaussian conditional autoregressive (CAR) framework for multi-trait association mapping in structured populations, where the effects attributable to kinship matrix is modeled via CAR and the population structure variables are included as covariates to adjust …
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An International Spatial Analysis of the Welfare Spending’s Influence on Measles Immunization
… among the variables of interest. Therefore, a conditional autoregressive model is also tested to account for spatial random effects. In the fourth manuscript, these results outline the findings from the model testing. Results: The final model finds that both the first dose of measles vaccine (B …
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Spatial analysis of ischemic heart disease in Manitoba
… models that modelled the spatial covariance with conditional autoregressive structures. The third objective was assessed by extending the spatial model to the temporal dimension by modeling the temporal covariance with random-walk covariance structures. Space-time interaction effects were assessed …
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Methods and applications for space-time data
… few points in time, a class of spatially varying autoregressive (SVAR) models compounded with conditional autoregressive (CAR) spatial correlation structures is proposed. The copula approach coupled with a flexible CAR formulation are employed to model the dependency between adjacent counties. …
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Integrative Approaches in Genomic Analysis: Advancing Epigenetic Prediction, Twas Methodology, and Cell-Type Deconvolution in Spatial Transcriptomics
… transformation for cell-type deconvolution with conditional autoregressive-based deconvolution (CARD). CARD assumes normality of the highly zero-inflated, count-valued spatial transcriptomic data, and the ZI-HGT transforms the data to better fit this assumption. Joined together, the ZI-HGT and …
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Spatiotemporal Modeling for Wildlife Demographic Analysis: Bridging Analysis to Waterfowl Conservation
… processes. In my third chapter, I use a conditional autoregressive model to estimate survival and harvest mortality from 1974 to 2023 of female and male mallards at the adult and juvenile age stages. Specifically, I studied populations in the Prairie Pothole Region (PPR) of the northern …
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