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 76 for “"incomplete data"”.
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Analysis based on incomplete data
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01
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Causal structure discovery from incomplete data
… causal DAG from observational and interventional data, when the intervention targets are either partially or completely unknown. We relate the algorithm to the recently introduced Joint Causal Inference framework. Finally, we evaluate the performance of the algorithm on synthetic datasets and …
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Learning discriminative models with incomplete data
… using multiple modalities, e.g. sensor data from video, audio, physiological changes etc. Often in real-world scenarios there can be incompleteness in the training data. There can be missing channels due to sensor failures in multi-sensory data and many data points in the training set …
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Knowledge transfer in vision tasks with incomplete data
… prevalent as to be left unwritten: all necessary data are available throughout the training process, the training and test data are independent and identically distributed (i.i.d.), and the dataset sampling sufficiently represent the test data of the model's usage scenario. Transfer learning …
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Nonparametric efficient estimation of prediction error for incomplete data models
Commonly accepted measures of prediction error, such as mean squared <br>error or R^2 typically fail to be identifiable with censored <br>observations. The Brier score is a loss function which is suitable for <br>the assessment of predictions made in terms of predicted probabilities <br>that are …
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Dealing with inconsistent and incomplete data in a semantic technology setting
Semantic and traditional databases are vulnerable to Inconsistent or Incomplete Data (IID). A data set stored in a traditional or semantic database is queried to retrieve record(s) in a tabular format. Such retrieved records can consist of many rows where each row contains an object and the …
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Iterative algorithms for optimal signal reconstruction and parameter identification given noisy and incomplete data
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1982.
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GAUSSIAN PROCESSES FOR 3D SHAPE MODELLING OF NOISY AND INCOMPLETE DATA. AN APPLICATION TO HUMAN EARS RECONSTRUCTION
… together with the extensive presence of missing data and outliers, originated from the data retrieval procedure. Gaussian Processes were recently proposed as a suitable framework for the formulation of shape modelling problems and have mostly been applied to the human face. We show that this is …
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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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Planar detection using modified expectation maximization
… planar detection in an image pair is cast as an incomplete data problem where the parameters to be estimated are the ones that define the homographies induced by the planar regions in the scene. This incomplete data problem motivates the employment of the Expectation Maximization (EM) algorithm. …
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Missing Responses in Generalized Linear Mixed Models Where the Missingness is Nonignorable
Missing data are common in many clinical studies. In this thesis, we review methods for analyzing incomplete data using generalized linear mixed models (GLMMs). GLMMs are widely used in clustered and longitudinal data analyses, where random effects are used to model subject or cluster specific …
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The information regularization framework for semi-supervised learning
… to algorithms for training the classifier from data that may be missing the class label. While traditional supervised classifiers already have the ability to cope with some incomplete data, the new type of classifiers do not view unlabeled data as an anomaly, and can learn from data sets in …
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Curve evolution and estimation-theoretic techniques for image processing
… algorithms designed to solve incomplete data problems in which part of the data is not observed, or hidden. These incomplete data problems are frequently encountered in image processing and computer vision. The basis of this framework is the marriage of the …
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Sunlight readability and luminance characteristics of light-emitting diode push button switches.
… that can adversely affect lighting quality. Incomplete data exists to educate consumers about potential differences in LED switch performance between different manufacturers. LED switches from four different manufacturers were tested for six attributes of lighting quality: average luminance …
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Parameter Estimation for Normally Distributed Grouped Data and Clustering Single-Cell RNA Sequencing Data via the Expectation-Maximization Algorithm
… estimates in problems involving missing data or latent variables. The EM algorithm can be applied to problems consisting of evidently incomplete data or missingness situations, such as truncated distributions, censored or grouped observations, and also to problems in which the missingness …
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Time series estimation in a spiked signal regime
Working with missing or incomplete data is a universal problem in all sciences. In meteorology, temperature data streams can contain missing values due to sensor malfunctions. In geophysical remote sensing, missing data can may be attributed to irregular global sampling by an orbiting spacecraft. …
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HAS THE SUN SET ON THE GOLDEN HOUR?
… edge of combat operations. A detailed review of data on the survivability of wounded servicemembers and civilians proved that there is no significant difference in survivability rates when the golden hour is preferred over TCCC. The 60-minute rule has been promoted based on opinion, anecdote, and …
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Surviving Reality: Survivor & Parasocial Interaction
… gender, age, and spokesperson selection. The data for this study was collected on the Internet website www.Survivorthesis.com. More than 450 respondents attempted the survey, but only 444 were viable due to incomplete data, repetition, and lack of proof of age. The results of the study found …
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Size and Power of Tests of Hypotheses on Parameters When Modeling Time-to-Event Data with the Lindley Distribution
… to times-to-event in time-to-event studies. Such data are often analyzed by choosing a model that allows the covariate information to be utilized in the analyses. The analysis proceeds by estimating parameters in the model and testing hypotheses about the parameters based on their estimates; …
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