Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 32 for “"Imputation methods"”.
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Evaluation of Imputation Methods Focusing on Categorical Outcomes
… from HIV positive subjects are incomplete. Imputation methods fill in educated guesses into the missing values in a dataset en abling the utilization of all collected information without discarding any observations. Several options are available and, in this work, the popular and freely …
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Partial least squares structural equation modelling with incomplete data. An investigation of the impact of imputation methods.
Despite considerable advances in missing data imputation methods over the last three decades, the problem of missing data remains largely unsolved. Many techniques have emerged in the literature as candidate solutions. These techniques can be categorised into two classes: statistical methods of …
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Topics in Bayesian adaptive clinical trial design using dynamic linear models and missing data imputation in logistic regression.
… with the missing data from among the numerous imputation methods that can be used. However, one might not know which imputation method is the best. The objective of this study is to evaluate the efficacy of five imputation methods. In Chapter Four, we have compared the performance of …
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Influence of missing explanatory variables and longitudinal assessments in breast cancer clinical trials
… to breast cancer clinical trials. In particular, imputation in the context of time-dependent covariates that may be informative missing data which is described has not been studied in detail in the statistical literature. Standard imputation methods from the statistical literature are described, …
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Imputation of Microsatellite Markers With Tag SNPs
… manner. For this reason, we seek to explore methods that can allow us to use the more readily available SNP genotype information to infer VNTR genotypes in nearby genomic regions. We focus in particular on imputing a VNTR polymorphism, 5-HTTLPR, in the promoter region of the serotonin …
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To Tolerate or To Impute Missing Values in V2X Communications Data?
… data. This thesis proposes three novel methods for imputing and tolerating missing data. The first two are novel imputation methods that are based on cooperative clustering and collaborative clustering. The latter is a missing-tolerant method that is an ensemble learning based on the …
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NBA Sleep Tracking Data Imputation
This thesis investigates imputation methods for nights of missing sleep wearable data from NBA Academy athletes. Sparsity in sleep tracking data arises as a result of behavioral non-compliance or device malfunction, hindering the NBA Academy's ability to provide actionable insights that improve …
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A restriction method for the analysis of discrete longitudinal missing data.
… a study. Many researchers often resort to ad hoc methods(e.g. case-deletion or mean imputation) when they are faced with missing data, which can lead to biased results. An alternative to these ad hoc methods is multiple imputation. Sources of missing data due to patient dropout in health related …
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Selected topics in statistical discriminant analysis.
… analysis: dimension reduction, regularization methods, and imputation methods. In Chapter 2 we first derive a new linear dimension-reduction method to determine a low-dimensional hyperplane that preserves or nearly preserves the separation of the individual populations and the Bayes probability …
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Large-scale inference and imputation for multi-tissue gene expression
… of scalable, non-linear, and flexible methods. This dissertation develops inference and imputation methods for the analysis of gene expression data, an immensely rich and complex biomedical data modality, enabling integration across multiple tissues. The imputation task can strongly …
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Using generative modelling in healthcare
… explored. We shall review the state-of-the-art methods that are employed when trying to detect genetic factors that affect gene expression, which is a core problem in genetics. We shall also present two popular classes of generative models, namely Variational Autoencoders (VAEs) and Generative …
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Machine-learning models for predicting drug approvals and clinical-phase transitions
… with over 140 features across 15 disease groups. Imputation methods are used to deal with missing data, allowing us to fully exploit the entire dataset, the largest of its kind. We achieve predictive measures of 0.74, 0.78, and 0.81 AUC for predicting transitions from phase 2 to phase 3, phase 2 …
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Topics in Deep Generative Modelling Mathematical and Computational Aspects of Diffusion Models and Generative Adversarial Networks
… comparison and theoretical analysis of various methods for learning conditional probability distributions using score-based diffusion models. We have proven results that offer a solid theoretical justification for one of the most effective estimators of the conditional score. Additionally, we …
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Quantile autoregression with censored data
… that the existing censored quantile regression methods produce empirically consistent estimator on QAR models when using only observed part of regressors. A new algorithm is proposed to improve a censored quantile autoregression (CQAR) estimator by adopting an idea of imputation methods. The …
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Robustness of Multiple Imputation under Missing at Random (MAR) Mechanism: A Simulation Study
… of missing data and applying inappropriate methods of analysis generates biased estimates and reduces power of study. It is very important for investigators to use appropriate methods of analysis to deal with missing data in order to maintain internal (power of study) and external …
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Missing data and multiple imputation: A sampling study with the SAGA cohort
… The aim of this thesis is to compare selected methods on the Stress-And Gene-Analysis (SAGA) cohort. Methods: Using: Complete case analysis (CCA), Single imputation using predictive mean matching (SI-PMM), Multiple imputation using predictive mean matching (MI-PMM) and Multiple imputation using …
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Informatics Strategies for Alzheimer’s Disease Research Through Analyzing Genetics and Neuroimaging Data
… for multi-modal integration, refining advanced imputation methods to manage incomplete data, and incorporating spatial and single-cell multi-omics data. These strategies promise to unravel the complex biological networks underlying complex chronic diseases and their co-occurring chronic …
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Clinical trend discovery and analysis of Taiwanese health insurance claims data
… were highly sparse. We attempted quantitative imputation methods to densify these matrices, but because the data was binary (indicating the presence of an event at a given time), categorical, and irregular, advanced imputation offered limited benefit. Nevertheless, we discovered interesting …
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HOW PERFORMANCE ON THE PASAT RELATE TO GLOBAL OUTCOME ON THE DISABILITY RATING SCALE FOLLOWING CLOSED-HEAD INJURY.
… were added to the statistical analysis through imputation. Participants and Method: Data from 169 individuals with complicated mild, moderate, and severe closed head injuries who participated in CPHS approved, NIH funded research that involved the collection of DRS data and neuropsychological …
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Genomic selection and quantitative trait loci mapping for Fusarium head blight resistance in wheat (Triticum aestivum L.)
… were tested, including: i) five genotypic imputation methods (random forest imputation – RFI, expectation maximization imputation – EMI, k-nearest neighbor imputation – KNNI, singular value decomposition imputation – SVDI and the mean imputation – MNI); ii) three statistical models (ridge …
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