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Visar resultat 1 - 5 av 6 avhandlingar som matchar ovanstående sökkriterier.

1. 1. Enhanced block sparse signal recovery and bayesian hierarchical models with applications

   Författare :Jianfeng Wang; Jun Yu; Anders Garpebring; Jia Li; Umeå universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Magnetic resonance imaging; Bayesian hierarchical models; Weather Research and Forecasting; Compressive sensing; Block sparsity; Multivariate isotropic symmetric a-stable distribution; q-ratio block constrained minimal singular value;

   Sammanfattning : This thesis is carried out within two projects ‘Statistical modelling and intelligentdata sampling in Magnetic resonance imaging (MRI) and positron-emission tomography(PET) measurements for cancer therapy assessment’ and ‘WindCoE -Nordic Wind Energy Center’ during my PhD study. It mainly focuses on applicationsof Bayesian hierarchical models (BHMs) and theoretical developments ofcompressive sensing (CS). LÄS MER

3. 2. Sparse Modeling of Grouped Line Spectra

   Författare :Ted Kronvall; Statistical Signal Processing Group; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; line spectra; parameter estimation; convex optimization; group-sparsity; block-sparsity; dictionary learning; ADMM; adaptive penalty; total variation; multi-pitch estimation; chroma; audio processing; TDOA; near-field localization; amplitude modulation;

   Sammanfattning : This licentiate thesis focuses on clustered parametric models for estimation of line spectra, when the spectral content of a signal source is assumed to exhibit some form of grouping. Different from previous parametric approaches, which generally require explicit knowledge of the model orders, this thesis exploits sparse modeling, where the orders are implicitly chosen. LÄS MER

4. 3. The Quest for Robust Model Selection Methods in Linear Regression

   Författare :Prakash Borpatra Gohain; Magnus Jansson; K.V.S Hari; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Model selection; information criterion; linear regression; sparsity; high dimensional; Electrical Engineering; Elektro- och systemteknik; Mathematical Statistics; Matematisk statistik;

   Sammanfattning : A fundamental requirement in data analysis is fitting the data to a model that can be used for the purpose of prediction and knowledge discovery. A typical and favored approach is using a linear model that explains the relationship between the response and the independent variables. LÄS MER

5. 4. Network models with applications to genomic data: generalization, validation and uncertainty assessment

   Författare :José Sánchez; Göteborgs universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Inverse covariance matrix; precision matrix; graphical models; high-dimension; low-sample; networks; sparsity; fused lasso; elastic net; cancer; TCGA pan cancer analysis; online resource; discriminant analysis; classification; networks;

   Sammanfattning : The aim of this thesis is to provide a framework for the estimation and analysis of transcription networks in human cancer. The methods we develop are applied to data collected by The Cancer Genome Atlas (TCGA) and supporting simulations are based on derived models in order to reflect real data structure. LÄS MER

7. 5. Classification models for high-dimensional data with sparsity patterns

   Författare :Annika Tillander; Tatjana Pavlenko; Daniel Thorburn; Patrik Ryden; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; High-dimensionality; supervised classification; classification accuracy; sparse; block-diagonal covariance structure; graphical Lasso; separation strength; discretization; Statistics; statistik;

   Sammanfattning : 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. LÄS MER