Search for dissertations about: "Multiple imputation"

Showing result 1 - 5 of 11 swedish dissertations containing the words Multiple imputation.

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

    University dissertation from Stockholm : Department of Statistics, Stockholm University

    Author : Nicklas Pettersson; Daniel Thorburn; Susanne Rässler; [2013]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Bayesian Bootstrap; Boundary Effects; External Information; Kernel estimation features; Local Balancing; Pólya Sampling; statistik; Statistics;

    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. Multiple Kernel Imputation : A Locally Balanced Real Donor Method

    University dissertation from Stockholm : Department of Statistics, Stockholm University

    Author : Nicklas Pettersson; Daniel Thorburn; Susanne Rässler; [2013]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Bayesian Bootstrap; Boundary Effects; External Information; Kernel estimation features; Local Balancing; Pólya Sampling; statistik; Statistics;

    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

  3. 3. Statistical inference with deep latent variable models

    University dissertation from Stockholm : Department of Statistics, Stockholm University

    Author : Najmeh Abiri; [2019]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Deep Learning; Generative Models; Variational Inference; Missing data; Imputation; Fysicumarkivet A:2019:Abiri;

    Abstract : Finding a suitable way to represent information in a dataset is one of the fundamental problems in Artificial Intelligence. With limited labeled information, unsupervised learning algorithms help to discover useful representations. READ MORE

  4. 4. Statistical modeling in international large-scale assessments

    University dissertation from Umeå : Umeå universitet

    Author : Inga Laukaityte; Marie Wiberg; Kenny Bränberg; Ewa Rolfsman; Bernard Veldkamp; [2016]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; multilevel model; plausible values; sampling weights; missing information; multiple imputation; non-monotone missing pattern; TIMSS; PISA; Statistics; statistik; pedagogik; Education;

    Abstract : This thesis contributes to the area of research based on large-scale educational assessments, focusing on the application of multilevel models. The role of sampling weights, plausible values (response variable imputed multiple times) and imputation methods are demonstrated by simulations and applications to TIMSS (Trends in International Mathematics and Science Study) and PISA (Programme for International Student Assessment) data. READ MORE

  5. 5. Bayesian Cluster Analysis Some Extensions to Non-standard Situations

    University dissertation from Stockholm : Statistiska institutionen

    Author : Jessica Franzén; Daniel Thorburn; Jukka Corander; [2008]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Cluster analysis; Clustering; Classification; Mixture model; Gaussian; Bayesian inference; MCMC; Gibbs sampler; Deviant group; Longitudinal; Missing data; Multiple imputation; SOCIAL SCIENCES Statistics; computer and systems science Statistics; SAMHÄLLSVETENSKAP Statistik; data- och systemvetenskap Statistik; statistik; Statistics;

    Abstract : The Bayesian approach to cluster analysis is presented. We assume that all data stem from a finite mixture model, where each component corresponds to one cluster and is given by a multivariate normal distribution with unknown mean and variance. READ MORE