Search for dissertations about: "high dimensional problem"
Showing result 1 - 5 of 231 swedish dissertations containing the words high dimensional problem.
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1. Optimal portfolios in the high-dimensional setting : Estimation and assessment of uncertainty
Abstract : Financial portfolios and diversification go hand in hand. Diversification is one of, if not, the best risk mitigation strategy there is. If an investment performs poorly, then it will not impact the performance of the portfolio much due to diversification. Modern Portfolio Theory (MPT) is a framework for constructing diversified portfolios. READ MORE
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2. Resampling in network modeling of high-dimensional genomic data
Abstract : Network modeling is an effective approach for the interpretation of high-dimensional data sets for which a sparse dependence structure can be assumed. Genomic data is a challenging and important example. In genomics, network modeling aids the discovery of biological mechanistic relationships and therapeutic targets. READ MORE
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3. Classification models for high-dimensional data with sparsity patterns
Abstract : Today's high-throughput data collection devices, e.g. spectrometers and gene chips, create information in abundance. However, this poses serious statistical challenges, as the number of features is usually much larger than the number of observed units. READ MORE
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4. Valid causal inference in high-dimensional and complex settings
Abstract : The objective of this thesis is to consider some challenges that arise when conducting causal inference based on observational data. High dimensionality can occur when it is necessary to adjust for many covariates, and flexible models must be used to meet convergence assumptions. The latter may require the use of a novel machine learning estimator. READ MORE
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5. Nearest Neighbor Classification in High Dimensions
Abstract : The simple k nearest neighbor (kNN) method can be used to learn from high dimensional data such as images and microarrays without any modification to the original version of the algorithm. However, studies show that kNN's accuracy is often poor in high dimensions due to the curse of dimensionality; a large number of instances are required to maintain a given level of accuracy in high dimensions. READ MORE