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.
Results
Showing 1 to 12 of 12 for “"Missing Data Imputation"”.
-
Missing data imputation in a clinical registry with deep generative models
Missing data is a common problem in all data driven algorithms. An incomplete dataset can bring bias to the trained model, or cause failures in the deployment of models that require a complete input. A clinical registry is a record of patients information about their health history, status and the …
-
Topics in Bayesian adaptive clinical trial design using dynamic linear models and missing data imputation in logistic regression.
… and, thus, it is not applicable for continuous data. The traditional design in Phase II determines if a new drug will be further tested in Phase III based on only drug efficacy and allocates an equal number of patients to each dosage, ignoring dose efficacy. Because of the limitations of …
-
Deep learning for grouped data
… applying deep learning algorithms to groups of data. My claim is that groups should be represented as random variables whose values should be inferred from data. This approach has the potential to unlock solutions in many important domains of machine learning, including disentangling the …
-
Machine learning for problems with missing and uncertain data with applications to personalized medicine
… real-world applications, we frequently encounter data which include missing and uncertain values. This thesis explores the problem of learning from missing and uncertain data with a focus on applications in personalized medicine. In the first chapter, we present a framework for classification when …
-
Novel Machine Learning Algorithms for Personalized Medicine and Insurance
… personalized care. As an increasing amount of data is being collected, computational performance is improved, and new algorithms are developed, machine learning has been viewed as the key analytical tool that will advance healthcare delivery. Nevertheless, until recently, despite the enthusiasm …
-
New algorithms in machine learning with applications in personalized medicine
… in areas such as health care where abundant data are increasingly being collected. However, imperfections in the data pose a major challenge to realizing their full potential: missing values, noisy observations, and unobserved counterfactuals all impact the performance of data-driven methods. …
-
Advances in Bayesian Machine Learning: From Uncertainty to Decision Making
… approximate inference to “big model $\times$ big data” regimes, many open challenges remain. For instance, how to properly quantify the parameter uncertainties for complicated, non-identifiable models (such as neural networks)? How to properly handle the uncertainties caused by missing data, and …
-
Statistical Modeling and Analysis of Bivariate Spatial-Temporal Data with the Application to Stream Temperature Study
… for analyzing bivariate spatial-temporal data in a stream temperature study. In the first part, I focus our analysis on the individual stream. A time varying coefficient model (VCM) is used to study the relationship between air temperature and water temperature for each stream. The time …
-
The Single Imputation Technique in the Gaussian Mixture Model Framework
Missing data is a common issue in data analysis. Numerous techniques have been proposed to deal with the missing data problem. Imputation is the most popular strategy for handling the missing data. Imputation for data analysis is the process to replace the missing values with any plausible values. …
-
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 …
-
Generalised Bayesian matrix factorisation models
… importance to the unsupervised analysis of data, with a colourful history more than a century long. Probabilistic models for matrix factorisation allow us to explore the underlying structure in data, and have relevance in a vast number of application areas including collaborative filtering, …
-
Modeling of Groundwater Heavy Metals and Methane Pollution for a Municipal Landfill Utilizing Data Driven and Numerical Modeling Techniques
… municipal landfill in a semi-arid climate. The data was collected from 5 groups of wells located upstream and downstream of the landfill in 2012 and 2015. The results represented that in 2015, Mn, U, As and Fe exceeded the Saskatchewan drinking water quality standard limits. However, in 2012 …