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Visar resultat 1 - 5 av 28 avhandlingar som matchar ovanstående sökkriterier.
1. Non-Gaussian Statistical Modelsand Their Applications
Sammanfattning : Statistical modeling plays an important role in various research areas. It provides away to connect the data with the statistics. Based on the statistical properties of theobserved data, an appropriate model can be chosen that leads to a promising practicalperformance. LÄS MER
2. Noise Convolution Models: Fluids in Stochastic Motion, Non-Gaussian Tempo-Spatial Fields, and a Notion of Tilting
Sammanfattning : The primary topic of this thesis is a class of tempo-spatial models which are rather flexible in a distributional sense. They prove quite successful in modeling (temporal) dependence structures and go beyond the limitation of Gaussian models, thus allowing for heavy tails and skewness. LÄS MER
3. Statistical analysis of non-Gaussian environmental loads and responses
Sammanfattning : The thesis deals mainly with offshore engineering related problems where the dominant source of uncertainty is related to the loading. Loads arise from environmental random processes; e.g. waves, currents and winds. LÄS MER
4. Spatial Mixture Models with Applications in Medical Imaging and Spatial Point Processes
Sammanfattning : 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. LÄS MER
5. Essays on Time Series Analysis : With Applications to Financial Econometrics
Sammanfattning : This doctoral thesis is comprised of four papers that all relate to the subject of Time Series Analysis.The first paper of the thesis considers point estimation in a nonnegative, hence non-Gaussian, AR(1) model. The parameter estimation is carried out using a type of extreme value estimators (EVEs). LÄS MER