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 131 for “"Missing Values"”.
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Missing values imputation and image registration for genetics applications
… first is in the setting of clustered data with missing values. We design an algorithm for imputing missing values using optimal recovery and derive an error bound for non-negative matrix factorization of the imputed data. Second, we consider missing values as erasure channels and show examples …
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Methods for the estimation of missing values in time series
… Since, the data are records taken through time, missing observations in time series data are very common. This occurs because an observation may not be made at a particular time owing to faulty equipment, lost records, or a mistake, which cannot be rectified until later. When one or more …
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To Tolerate or To Impute Missing Values in V2X Communications Data?
… the main strategies for the treatment of missing values to find out the best match for misbehavior detection with incomplete V2X communication data. This thesis proposes three novel methods for imputing and tolerating missing data. The first two are novel imputation methods that are based …
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Missing values in covariance in the case of the randomized block
The formula and theory for estimating a missing value in the case of covariance in a randomized block has been presented in this paper. It has also been found that the formula given corresponds to Yates’ formula for a missing value in a randomized block when there is only one variable present in …
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Making diffusion work for you: Classification sans text, finding culprits and filling missing values
Can we find people infected with the flu virus even though they did not visit a doctor? Can the temporal features of a trending hashtag or a keyword indicate which topic it belongs to without any textual information? Given a history of interactions between blogs and news websites, can we predict …
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Develop innovative methodology to optimally fill in missing values and predict progression on multiple sclerosis
… MS progression, is effectively managing missing data in MS datasets. This study introduces an innovative sequential Multi-Imputation (MI) bootstrapping method to address the challenge of missing data in MS datasets. Initially, several ML algorithms, including k-Nearest Neighbors (kNN), …
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The estimation of missing values in hydrological records using the EM algorithm and regression methods
… is to review existing methods for estimating missing values in rainfall records and to propose a number of new procedures. Two classes of methods are considered. The first is based on the theory of variable selection in regression. Here the emphasis is on finding efficient methods to identify …
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Analysis of variance of a balanced incomplete block design with missing observations
… in this paper is that of estimating several missing values and analyzing the resulting augmented data in a balanced incomplete block design. The estimates are obtained by Yates' procedure of minimizing the error sum of squares. Explicit formulae are obtained for all cases involving not more …
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A deep learning approach to real-time short-term traffic speed prediction with spatial-temporal features
… part of our dataset contains a large amount of missing values, we study the effectiveness of a multi-view approach to imputing the missing values so that various prediction models can apply. Experimental results showed that the performance of the traffic speed prediction model improved …
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Seven methods of handling missing data using samples from a national data base
The effectiveness of seven methods of handling missing data was investigated in a factorial design using random samples selected from the National Education Longitudinal Study of 1988 (NELS-88). Methods evaluated were listwise deletion, pairwise deletion, mean substitution, Buck's procedure, mean …
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Handling missing data in analyses of the UK women's cohort study
Missing values are a problem in large-scale surveys with extensive questionnaires. The analysis of the complete records may yield inferences substantially different from those that would be obtained had no data been missing. The aim of this dissertation is to critically examine ways of handling …
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Essays on Real Estate Finance and Machine Learning
… useful in real estate research when dealing with missing data. I demonstrate impressive gains in out- of-sample predictions once I absorb observations with missing values. More importantly, I find that researchers may draw the wrong conclusions if they solely rely on complete but smaller data …
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Practical approaches to mining of clinical datasets : from frameworks to novel feature selection
… complexities and uncertainties in the form of missing values, class imbalances and high dimensionality. The research in this thesis was motivated by these challenges to minimise these problems whilst, at the same time, maximising classification performance of data and also selecting the …
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High-dimensional covariance estimation with applications to functional genomics
… high-dimensional and noisy data, possibly with missing values. In such context, there is a need to develop scalable and robust estimation methods that can improve inference by, for example, taking advantage of the many sources of external information available in public repositories. This thesis …
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Inductive classifier learning from data: An extended Bayesian belief function approach
… problems in inductive learning, including noise, missing values, small samples, inter-attribute dependency, and irrelevant or partially relevant attributes, all of which are characteristics of real-world induction tasks.
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Imputing age at death for the deceased using household relationships
… available on methods applied to replacing missing values for non-responses in surveys or censuses so that these methods are implemented using sound data is sparse. The National Income and Dynamics Study (NIDS) household dataset includes the relationship of the deceased to the head of …
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Artificial intelligence-assisted data analysis with BayesDB
… real-world datasets, problems often arise due to missing data or errors from black-box predictive models that are difficult to understand or explain in terms of the model's inputs. This thesis explores the applicability of BayesDB, a probabilistic programming platform for data analysis, to three …
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Retail Price Time Series Imputation
… datasets are typically incomplete due to missing observations and missing values for important attributes. In this thesis, we consider the problem of filling in missing values in crowdsourced, regular, discrete, retail price time series datasets using data imputation methods. We introduce …
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Using Data Augmentation and Stochastic Differential Equations in Spatio Temporal Modeling
… is indeed how to manage the large amount of missing information. Data augmentation techniques are frequently used to infer about missing values, unobserved or latent processes, approximation of continuous time processes that are discretely observed.</p><p>The literature treating the inference …
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Evaluation of the Effect of the Clinical-Decision-Support Systems on Diabetes Management: A Multivariate Meta-Analysis Comparison with Univariate Meta-Analysis
… size, between-study correlation, and percent of missing data) have a significant effect on meta-analysis estimates and whether using univariate or multivariate meta-analysis would produce different estimates.</p> <p>The second goal of this study was to evaluate the effect of clinical decision …
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