Search for dissertations about: "bandwidth selection"
Showing result 1 - 5 of 43 swedish dissertations containing the words bandwidth selection.
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1. Efficient training of interpretable, non-linear regression models
Abstract : Regression, the process of estimating functions from data, comes in many flavors. One of the most commonly used regression models is linear regression, which is computationally efficient and easy to interpret, but lacks in flexibility. READ MORE
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2. Extensions of the kernel method of test score equating
Abstract : This thesis makes contributions within the area of test score equating and specifically kernel equating. The first paper of this thesis studies the estimation of the test score distributions needed in kernel equating. READ MORE
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3. Continuous-Time Models in Kernel Smoothing
Abstract : This thesis consists of five papers (Papers A-E) treating problems in non-parametric statistics, especially methods of kernel smoothing applied to density estimation for stochastic processes (Papers A-D) and regression analysis (Paper E). A recurrent theme is to, instead of treating highly positively correlated data as ``asymptotically independent'', take advantage of local dependence structures by using continuous-time models. READ MORE
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4. Simulation and Estimation of Diffusion Processes : Applications in Finance
Abstract : Diffusion processes are the most commonly used models in mathematical finance, and are used extensively not only by academics but also practitioners. Nowadays a wide range of models, that can capture many of the effects observed in financial markets, are available. READ MORE
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5. Access selection in multi-system architectures : cooperative and competitive contexts
Abstract : Future wireless networks will be composed of multiple radio access technologies (RATs). To benefit from these, users must utilize the appropriate RAT, and access points (APs). In this thesis we evaluate the efficiency of selection criteria that, in addition to path-loss and system bandwidth, also consider load. READ MORE