Search for dissertations about: "missing covariates"

Showing result 1 - 5 of 6 swedish dissertations containing the words missing covariates.

  2. 1. Methodology for Handling Missing Data in Nonlinear Mixed Effects Modelling

    Author : Åsa M. Johansson; Mats O. Karlsson; Andrew C. Hooker; Leon Aarons; Uppsala universitet; []
    Keywords : MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; Pharmacometrics; population models; censored observations; missing covariates; missing dependent variable; missing data mechanism; missing completely at random MCAR ; missing at random MAR ; missing not at random MNAR ; estimation algorithms; Pharmaceutical Science; Farmaceutisk vetenskap;

    Abstract : To obtain a better understanding of the pharmacokinetic and/or pharmacodynamic characteristics of an investigated treatment, clinical data is often analysed with nonlinear mixed effects modelling. The developed models can be used to design future clinical trials or to guide individualised drug treatment. READ MORE

  4. 2. Uncertainty intervals and sensitivity analysis for missing data

    Author : Minna Genbäck; Xavier de Luna; Elena Stanghellini; Arvid Sjölander; Umeå universitet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; missing data; missing not at random; non-ignorable; set identification; uncertainty intervals; sensitivity analysis; self reported health; average causal effect; average causal effect on the treated; mixed-effects models; Statistics; statistik;

    Abstract : In this thesis we develop methods for dealing with missing data in a univariate response variable when estimating regression parameters. Missing outcome data is a problem in a number of applications, one of which is follow-up studies. READ MORE

  5. 3. Interpretable machine learning models for predicting with missing values

    Author : Lena Stempfle; Chalmers tekniska högskola; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; missing values; Machine learning; healthcare; interpretable machine learning;

    Abstract : Machine learning models are often used in situations where model inputs are missing either during training or at the time of prediction. If missing values are not handled appropriately, they can lead to increased bias or to models that are not applicable in practice without imputing the values of the unobserved variables. READ MORE

  6. 4. Valid causal inference in high-dimensional and complex settings

    Author : Niloofar Moosavi; Xavier de Luna; Jenny Häggström; Edward Kennedy; Umeå universitet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Causal inference; high dimension; sensitivity analysis; variable selection; convolutional neural network; semiparametric efficiency bound; Statistics; statistik;

    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

  7. 5. Mathematical programming for optimal probability weighting

    Author : Michele Santacatterina; Karolinska Institutet; Karolinska Institutet; []
    Keywords : ;

    Abstract : In spite of the fact that probability weighting is widely used in statistics to correct for unequal sampling, control for confounding, and handle missing data, it has two main limitations. First, statistical inferences may be inefficient in the presence of extreme probability weights. READ MORE