Search for dissertations about: "vector regression"
Showing result 1 - 5 of 55 swedish dissertations containing the words vector regression.
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1. Regression methods in multidimensional prediction and estimation
Abstract : In regression with near collinear explanatory variables, the least squares predictor has large variance. Ordinary least squares regression (OLSR) often leads to unrealistic regression coefficients. Several regularized regression methods have been proposed as alternatives. READ MORE
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2. Weighted Regression with Application to Array Antennas
Abstract : A nonlinear system can be modelled with a simple linear model ifthe model is only valid locally. This can be done by assigningweights to the estimation data, as a function of the distance tothe modelled point. The weighting is here used to develop adirection-dependent calibration method for array antennas. READ MORE
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3. Group-Sparse Regression : With Applications in Spectral Analysis and Audio Signal Processing
Abstract : This doctorate thesis focuses on sparse regression, a statistical modeling tool for selecting valuable predictors in underdetermined linear models. By imposing different constraints on the structure of the variable vector in the regression problem, one obtains estimates which have sparse supports, i.e. READ MORE
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4. Failure diagnostics using support vector machine
Abstract : Failure diagnostics is an important part of condition monitoring aiming to identify incipient failures in early stages. Accurate and efficient failure diagnostics can guarantee that the operator makes the correct maintenance decision, thereby reducing the maintenance costs and improving system availability. READ MORE
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5. Bias approximation and reduction in vector autoregressive models
Abstract : In the last few decades, vector autoregressive (VAR) models have gained tremendous popularity as an all-purpose tool in econometrics and other disciplines. Some of their most prominent uses are for forecasting, causality tests, tests of economic theories, hypothesis-seeking, data characterisation, innovation accounting, policy analysis, and cointegration analysis. READ MORE