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 20 for “"Mean Vector"”.
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Mahalanobis kernel-based support vector data description for detection of large shifts in mean vector
… Creation of the K chart is based on Support Vector Data Description (SVDD), a popular data classifier method inspired by Support Vector Machine (SVM). As any methods associated with SVM, SVDD benefits from a wide variety of choices of kernel, which determines the effectiveness of the whole …
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Multivariate control charts for the mean vector and variance-covariance matrix with variable sampling intervals
… Multivariate control charts for monitoring the mean vector, for monitoring variance-covariance matrix and for simultaneously monitoring the mean vector and the variance-covariance matrix of a process with a multivariate normal distribution are investigated. A variable sampling interval (VSI) …
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GLR Control Charts for Monitoring the Mean Vector or the Dispersion of a Multivariate Normal Process
… dissertation discusses the monitoring of the mean vector and the covariance matrix of MN processes. The first part of this dissertation develops a statistical process control (SPC) chart based on a generalized likelihood ratio (GLR) statistic to monitor the mean vector. The performance of the …
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CUSUM Generalized Variance Charts
… commonly recommended charts for monitoring the mean vector are affected by a shit in the covariance matrix. As in the univariate case, a chart for monitoring for a change in the covariance matrix should be examined first before examining the chart used to monitor for a change in the mean …
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A Performance Analysis of the Minimax Multivariate Quality Control Chart
… a unique combination of quality characteristic mean vector and associated covariance matrix. The three different charts under consideration differ in the number of quality characteristic variables of concern. In each case, without loss of generality the in-control quality characteristic mean …
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Robust MEWMA-type Control Charts for Monitoring the Covariance Matrix of Multivariate Processes
… follow a multivariate normal distribution with mean vector " and covariance matrix •, but this is rarely satisfied in practice. Some robust control charts have been developed to monitor the mean and variance of univariate processes, or the mean vector " of multivariate processes, but the …
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Monitoring for a Shift in a Process Covariance Matrix Using the Generalized Variance
… commonly recommended charts for monitoring the mean vector are affected by a shift in the covariance matrix. As in the univariate case, a chart for monitoring for a change in the covariance matrix should be examined first before examining the chart used to monitor for a change in the mean …
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Performance of Bootstrap Confidence Region For Binomial Distribution With Unknown Parameters p and m
… have moments of all orders, I cannot obtain the mean, variance, and covariance for these estimators. Thus, the Delta Method is used to derive the asymptotic normality of the joint distribution of the Method of Moments estimators. After finding the estimators pˆ, and mˆ , I will work on the …
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Failure time analyses for data collected from independent groups of correlated individuals
… intervals. For each time interval, a separate vector of binary responses is constructed for each group, consisting of the responses for individuals belonging to the group who are at risk at the beginning of the interval and not censored during the interval. The elements of the corresponding …
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A noise detection, noise-motion separation and a cancer recognition theory and algorithm
… subject to one type of noise, of two frames, has mean vector equal to (0,0,0), and variance covariance matrix with relatively small variances, for the (R, G, B) difference values. The other type of noise is a result of disturbance of the light equilibrium due to motion in neighboring or nearby …
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A Computational Study in the Detection of English–Spanish Code-Switches
… uses Doc2Vec, where the model uses the mean vector of each document to learn and distinguish between code-switched and monolingual data. Each of these experiments are performed twice, once with tweets and once with the Miami Bangor corpus. The results show that the WANN model performs …
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Contributions to Profile Monitoring and Multivariate Statistical Process Control
… variable or the multivariate distribution of a vector of quality variables. Rather, a series of measurements are taken across some continuum, such as time or space, to create a profile. The profile determines the product quality at that sampling period. We propose Phase I methods to analyze …
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Dense Optical Flow Estimation using Diffusion Distances
… By considering systems of correlation vectors from image neigbourhoods, we also increase the smoothness of the estimate. The present work compares several smoothing principles, including the vector mean, vector median, marginal median which are based on both the maximum correlation and …
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Portfolio selection using Random Matrix theory and L-Moments
… between them. Historically, the sample mean vector and variance-covariance matrix have been used for this purpose. However, estimation errors result in the optimised portfolios performing poorly out-of-sample. This dissertation considers two approaches to obtaining a more robust estimate …
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Geometries of word embeddings
… postprocessing technique -- eliminate the common mean vector and a few top dominating directions from the word vectors -- that achieves better performances on a variety of standard benchmarks than the original ones. Sentences, as a sequence of words, are also important semantic units of natural …
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The analysis of directional data: Performance comparisons of randomization and parametric methods
… collected initially in the form of unit vectors, or with vector-valued observations that can be reduced to that form through some type of preliminary normalization. Although there have been advances in the analysis of directional data in recent years, the collection of methods available …
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Process Monitoring with Multivariate Data:Varying Sample Sizes and Linear Profiles
… efficient in detecting changes in the process mean vector than standard control charts that do not use sequential sampling. Also the situation is considered where different variables may have different measurement costs. MEWMA control charts with unequal sample sizes based on differing …
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Machine Learning Solutions for Emotional Speech: Exploiting the Information of Individual Annotations
… scale. We train the deep network to predict the mean vector of this distribution instead of single label. The second challenge we address is the limited examples from minority emotional for training. We argue that individual labels convey more information than the consensus labels. We present a …
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Structural Analysis of the Northwest Wind Mountain Quadrangle, New Mexico: Proterozoic Shearing to Cenozoic Brittle Faulting in the Burro Mountains
… of oblique-slip (normal or reverse) motion. The mean vector of the mapped faults is 054<sup>o</sup>/26<sup>o </sup>(N54<sup>o</sup>E, 26<sup>o</sup>SE). The faults have two dominant orientations, northeast-southwest and northwest-southeast, making up 76% of all faults recorded. The complex …
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PERFORMANCE OF THE TWO SAMPLE LIKELIHOOD RATIO TEST UNDER A NESTED DIRICHLET: A SIMULATION STUDY
… of the LRT under varying sample sizes, mean vectors, and precision parameters, providing insight into its robustness and applicability.</p>