This master thesis compares the forecasting ability of Transfer function models and ARIMAX-models on monthly groundwater-level data from Sweden. The results are also compared to findings from earlier reports on the same data but with different models. Precipitation and the four seasons are used as covariates when fitting the models. Performance of the models were evaluated by fitting the models to the data excluding the last three years and, then, comparing the forecasts to the observations.

Random choice theory has traditionally modeled choices over a -nite number of options. This thesis generalizes the literature by studyingthe limiting behavior of choice models as the number of optionsapproach a continuum.The thesis uses the theory of random elds, extreme value theoryand point processes to calculate this limiting behavior. For a numberof distributional assumptions, we can give analytic expressions forthe limiting probability distribution of the characteristics of the bestchoice. In addition, we also outline a straightforward extension to ourtheory which would signicantly relax the distributional assumptionsneeded to derive analytical results.Some examples from commuting research are discussed to illustratepotential applications of the theory.

Images as important sources of information, powered by the newest robotic microscopy technologies, make the volume of data available larger than ever before. From the images, hundreds of variables can be calculated. The medical research group within the HASTE project conducts a LNP (Lipid Nano-particle) experiment that is designed to transport a drug to target cells. In the LNP experiment, microscopy images are taken over time to record if the drug is uptaken. We propose that the non-parametric change point analysis can be used to identify the variable which shows the earliest state change (potentially signifying the drug uptake) among all variables calculated from the images. Two algorithms for non-parametric change point analysis, an agglomerative and a divisive, are studied through simulation leading us to implement the agglomerative algorithm on the LNP experiment data. Furthermore, the simulation results show that the prominent variable detection accuracy improves when more time points are included in the experiment. In the application, correlation is most likely to be detected as the sole prominent variable.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.