Search for dissertations about: "Statistics for Spatial Data."

Showing result 1 - 5 of 78 swedish dissertations containing the words Statistics for Spatial Data..

  1. 1. Spatial Mixture Models with Applications in Medical Imaging and Spatial Point Processes

    University dissertation from Gothenburg : Chalmers University of Technolog

    Author : Anders Hildeman; Göteborgs universitet.; Gothenburg University.; [2017]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Non-Gaussian; Bayesian level set inversion; Point processes; Substitute CT; Finite mixture models; Spatial statistics; Gaussian fields;

    Abstract : Finite mixture models have proven to be a great tool for both modeling non-standard probability distributions and for classification problems (using the latent variable interpretation). In this thesis we are building spatial models by incorporating spatially dependent categorical latent random fields in a hierarchical manner similar to that of finite mixture models. READ MORE

  2. 2. Spatial Mixture Models with Applications in Medical Imaging and Spatial Point Processes

    University dissertation from Gothenburg : Chalmers tekniska högskola

    Author : Anders Hildeman; [2017]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Non-Gaussian; Bayesian level set inversion; Point processes; Spatial statistics; Substitute CT; Finite mixture models; Gaussian fields;

    Abstract : Finite mixture models have proven to be a great tool for both modeling non-standard probability distributions and for classification problems (using the latent variable interpretation). In this thesis we are building spatial models by incorporating spatially dependent categorical latent random fields in a hierarchical manner similar to that of finite mixture models. READ MORE

  3. 3. Spatial analysis and modeling of nerve fiber patterns

    University dissertation from Göteborg : Chalmers tekniska högskola

    Author : Claes Andersson; Göteborgs universitet.; Gothenburg University.; [2018]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Bayesian field theory; Diabetic neuropathies; Multilevel models Statistics ; Institutionen för matematiska vetenskaper. Tillämpad matematik och statistik.; CTH; hierarchical models; Bayesian estimation; epidermal nerve fibers; cluster processes; spatial point processes; diabetic neuropathy;

    Abstract : Diabetic neuropathy is a condition associated with diabetes affecting the epidermal nerve fibers (ENFs). This thesis presents analysis methods and models for ENF data, with two main puroposes: to find early signs of diabetic neuropathy and to characterize how this condition changes the nerve fiber structure. READ MORE

  4. 4. Spatial sampling and prediction

    University dissertation from Umeå : Umeå universitet

    Author : Lina Schelin; Umeå universitet.; [2012]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Auxiliary variables; Censoring; Inclusion probabilities; Kriging; Local pivotal method; Minimum detection limit; Prediction intervals; Representative sample; Spatial process; Spatial sampling; Semiparametric bootstrap; Mathematical Statistics; matematisk statistik;

    Abstract : This thesis discusses two aspects of spatial statistics: sampling and prediction. In spatial statistics, we observe some phenomena in space. Space is typically of two or three dimensions, but can be of higher dimension. READ MORE

  5. 5. On flexible random field models for spatial statistics: Spatial mixture models and deformed SPDE models

    University dissertation from Umeå : Umeå universitet

    Author : Anders Hildeman; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Spatial statistics; Significant wave height; Spatial mixture model; Stochastic partial differential equation; Log-Gaussian Cox process; Point process; Gaussian random field; Substitute-CT;

    Abstract : Spatial random fields are one of the key concepts in statistical analysis of spatial data. The random field explains the spatial dependency and serves the purpose of regularizing interpolation of measured values or to act as an explanatory model. READ MORE