Search for dissertations about: "Kernel"

Showing result 1 - 5 of 181 swedish dissertations containing the word Kernel.

  1. 1. Multiple Kernel Imputation A Locally Balanced Real Donor Method

    University dissertation from Stockholm : Department of Statistics, Stockholm University

    Author : Nicklas Pettersson; Stockholms universitet.; [2013]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; statistik; Statistics; Bayesian Bootstrap; Boundary Effects; External Information; Kernel estimation features; Local Balancing; Pólya Sampling;

    Abstract : We present an algorithm for imputation of incomplete datasets based on Bayesian exchangeability through Pólya sampling. Each (donee) unit with a missing value is imputed multiple times by observed (real) values on units from a donor pool. The donor pools are constructed using auxiliary variables. READ MORE

  2. 2. Continuous-Time Models in Kernel Smoothing

    University dissertation from Centre for Mathematical Sciences, Lund University

    Author : Martin Sköld; Lunds universitet.; Lund University.; [1999]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; deconvolution; errors-in-variables; continuous time; dependent data; bandwidth selection; asymptotic variance; Density estimation; kernel smoothing; size bias.; Mathematics; Matematik;

    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

  3. 3. Contributions to Kernel Equating

    University dissertation from Uppsala : Acta Universitatis Upsaliensis

    Author : Björn Andersson; Uppsala universitet.; [2014]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; observed-score test equating; item response theory; R; equipercentile equating; asymptotic standard errors; non-equivalent groups with anchor test design; Statistics; Statistik;

    Abstract : The statistical practice of equating is needed when scores on different versions of the same standardized test are to be compared. This thesis constitutes four contributions to the observed-score equating framework kernel equating. READ MORE

  4. 4. Efficient Image Retrieval with Statistical Color Descriptors

    University dissertation from Linköping : Linköping University Electronic Press

    Author : Linh Viet Tran; Linköpings universitet.; Linköpings universitet.; [2003]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; color properties; images; statistical; content-based image retrieval CBIR ; non-parametric density estimators; image database; Kernel; Gram-Schmidt; geometry-based; SOCIAL SCIENCES Statistics; computer and systems science Informatics; computer and systems science Information technology; SAMHÄLLSVETENSKAP Statistik; data- och systemvetenskap Informatik; data- och systemvetenskap Informationsteknologi;

    Abstract : Color has been widely used in content-based image retrieval (CBIR) applications. In such applications the color properties of an image are usually characterized by the probability distribution of the colors in the image. READ MORE

  5. 5. System identification with input uncertainties an EM kernel-based approach

    University dissertation from Stockholm : KTH Royal Institute of Technology

    Author : Riccardo Sven Risuleo; KTH.; [2016]
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Electrical Engineering; Elektro- och systemteknik;

    Abstract : Many classical problems in system identification, such as the classical predictionerror method and regularized system identification, identification of Hammersteinand cascaded systems, blind system identification, as well as errors-in-variablesproblems and estimation with missing data, can be seen as particular instancesof the general problem of the identification of systems with limited information.In this thesis, we introduce a framework for the identification of linear dynamicalsystems subject to inputs that are not perfectly known. READ MORE