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Showing result 1 - 5 of 12 swedish dissertations matching the above criteria.

2. 1. A Non-Stationary perspective on the European and Swedish Business Cycle

   Author : Louise Holm; Högskolan i Skövde; []
   Keywords : SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; Business cycles; business cycle dating; non-parametric smoothing; non-stationarity; recession prediction; interest rate spread; binary respons models; Business and economics; Ekonomi; Humanities and Social sciences; Humaniora-samhällsvetenskap; Business cycles; business cycle dating; non-parametric smoothing; non-stationarity; recession prediction; interest rate spread; binary response models.;

   Abstract : Business cycles, the ups and downs observed somewhat simultaneously in numerous macroeconomic variables in an economy and often measured using real GDP, are important and, despite much economic research, still incom- pletely understood. Dating the business cycle has always been of interest in macroeconomic research. READ MORE

4. 2. Stochastic modelling and analysis of early mouse development

   Author : Sofia Tapani; Göteborgs universitet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; correlation; CUSUM; mouse; non-stationarity; pronucleus; stochastic differential equation; time series; wavelet decomposition; yeast; CUSUM;

   Abstract : The aim of this thesis is to model and describe dynamical events for biological cells using statistical and mathematical tools. The thesis includes five papers that all relate to stochastic modelling of cells. READ MORE

5. 3. Observational Uncertainties in Water-Resources Modelling in Central America : Methods for Uncertainty Estimation and Model Evaluation

   Author : Ida Westerberg; Sven Halldin; Jan Seibert; Lars-Christer Lundin; Chong-Yu Xu; Deliang Chen; Alberto Montanari; Uppsala universitet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; Central America; Discharge; Flow-duration curve; Fuzzy regression; GLUE; Model evaluation; Non-stationarity; Observational uncertainty; Precipitation; Quality control; Rating curve; Regionalisation; Uncertainty estimation; Ungauged basins; Water resources; Avbördningskurva; Centralamerika; GLUE; icke-stationaritet; kvalitetskontroll; modellutvärdering; nederbörd; observationsosäkerheter; avrinningsområden utan vattenfö-ringsdata; oskarp regression; osäkerhetsuppskattning; regionalisering; varaktighetskurva; vattenföring; vattenresurser; Hydrology; Hydrologi; Hydrology; Hydrologi;

   Abstract : Knowledge about spatial and temporal variability of hydrological processes is central for sustainable water-resources management, and such knowledge is created from observational data. Hydrologic models are necessary for prediction for time periods and areas lacking data, but are affected by observational uncertainties. READ MORE

6. 4. Ion dynamics and structure of collisionless shocks

   Author : Andreas Johlander; Andris Vaivads; Rami Vainio; Uppsala universitet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES;

   Abstract : Shock waves are responsible for slowing down and heating supersonic flows. In collisionless space plasmas, shocks are able to accelerate particles to very high energies. We study injection of suprathermal ions at Earth’s quasi- parallel shock using high time resolution data from the Cluster spacecraft. READ MORE

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

   Author : Anders Hildeman; Chalmers tekniska högskola; []
   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