Search for dissertations about: "Missing values"

Showing result 1 - 5 of 68 swedish dissertations containing the words Missing values.

  1. 1. 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

  2. 2. Rainfall-Runoff Modelling Using Artificial Neural Networks (ANNs)

    University dissertation from Department of Water Resources Engineering, Lund Institute of Technology, Lund University

    Author : Aman Mohammad Kalteh; [2007]
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Hydrogeology; geographical and geological engineering; Hydrogeologi; teknisk geologi; teknisk geografi; Self-organizing map; Feed-forward multilayer perceptron; Forecasting; Hydrological modelling; Missing values; Rainfall-runoff modelling; Estimation; Artificial neural networks;

    Abstract : Over the last decades or so, artificial neural networks (ANNs) have become one of the most promising tools for modelling hydrological processes such as rainfall-runoff processes. In most studies, ANNs have been demonstrated to show superior result compared to the traditional modelling approaches. READ MORE

  3. 3. Classification and Computational Methods in Gene Expression Data Analysis

    University dissertation from Department of Theoretical Physics, Lund University

    Author : Cecilia Ritz; [2007]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Bioinformatik; medicinsk informatik; Bioinformatics; medical informatics; biomathematics biometrics; missing values; leukemia; cDNA microarray data; supervised classification; breast cancer; prognostic markers; biomatematik;

    Abstract : The technology of cDNA microarrays has given us the possibility to monitor the state of cells by measuring the activity of thousands of genes simultaneously. This high-throughput techniqe has in cancer research allowed exploratory studies of molecular mechanisms behind for example metastasis and response to therapy. READ MORE

  4. 4. Truncation and missing family links in population-based Registers

    University dissertation from Stockholm : Karolinska Institutet, Department of Medical Epidemiology and Biostatistics

    Author : Monica Leu; Karolinska Institutet.; Karolinska Institutet.; [2008]
    Keywords : MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; Left-trunction; Swedish Cancer Registry; MultiGeneration Register; family history; misclassification; simulation; Poplab;

    Abstract : Studies of familial aggregation of disease routinely use linked population registers to construct retrospective cohorts. Although such resources have provided numerous estimates of familial risk, little is known regarding the sensitivity of the estimates to assumed disease models and incompleteness of the data, such as truncation and/or missing family links. READ MORE

  5. 5. Statistical inference with deep latent variable models

    University dissertation from Stockholm : Karolinska Institutet, Department of Medical Epidemiology and Biostatistics

    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