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The multivariate one-way classification model with random effectsSchott, James Robert, January 1981 (has links)
Thesis (Ph. D.)--University of Florida, 1981. / Description based on print version record. Typescript. Vita. Includes bibliographical references (leaves 108-109).
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Ecological structure of South Korea an ecological factor analysis of cities and rural communities in South Korea in 1969 /Moon, Suk-Nam. January 1974 (has links)
Thesis--Stockholm. / Extra t.p. with thesis statement inserted. Bibliography: p. 135-141.
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Using external information for statistical process control /Yoon, Seongkyu. January 2001 (has links)
Thesis (Ph.D.) -- McMaster University, 2001. / Includes bibliographical references (leaves 169-180). Also available via World Wide Web.
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Analysis of data from a double-blind, placebo-controlled randomised clinical trial for the treatment of strokeTsang, Wai-yin. January 1993 (has links)
Thesis (M.Soc.Sc.)--University of Hong Kong, 1993. / Includes bibliographical references. Also available in print.
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Multivariate compound point processes with driftsZhou, Huajun, January 2006 (has links) (PDF)
Thesis (Ph. D.)--Washington State University, August 2006. / Includes bibliographical references (p. 67-68).
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Multivariate and univariate analyses of the geographic variation within Etheostoma flabellare (Pisces: Percidae) of eastern North America /McGeehan, Lawrence T. January 1985 (has links)
Thesis (Ph. D.)--Ohio State University, 1985. / Includes bibliographical references (leaves 142-148). Available online via OhioLINK's ETD Center.
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Infestation Detection in Wild Blueberries Using Near Infrared Spectra and Multivariate Data AnalysisPeshlov, Boyan N. January 2008 (has links) (PDF)
No description available.
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High-dimensional covariance matrix estimation with application to Hotelling's testsDong, Kai 31 August 2015 (has links)
In recent years, high-dimensional data sets are widely available in many scientific areas, such as gene expression study, finance and others. Estimating the covariance matrix is a significant issue in such high-dimensional data analysis. This thesis focuses on high-dimensional covariance matrix estimation and its application. First, this thesis focuses on the covariance matrix estimation. In Chapter 2, a new optimal shrinkage estimation of the covariance matrices is proposed. This method is motivated by the quadratic discriminant analysis where many covariance matrices need to be estimated simultaneously. We shrink the sample covariance matrix towards the pooled sample covariance matrix through a shrinkage parameter. Some properties of the optimal shrinkage parameter are investigated and we also provide how to estimate the optimal shrinkage parameter. Simulation studies and real data analysis are also conducted. In Chapter 4, we estimate the determinant of the covariance matrix using some recent proposals for estimating high-dimensional covariance matrix. Specifically, a total of nine covariance matrix estimation methods will be considered for comparison. Through extensive simulation studies, we explore and summarize some interesting comparison results among all compared methods. A few practical guidelines are also made on the sample size, the dimension, and the correlation of the data set for estimating the determinant of high-dimensional covariance matrix. Finally, from a perspective of the loss function, the comparison study in this chapter also serves as a proxy to assess the performance of the covariance matrix estimation. Second, this thesis focuses on the application of high-dimensional covariance matrix estimation. In Chapter 3, we consider to estimate the high-dimensional covariance matrix based on the diagonal matrix of the sample covariance matrix and apply it to the Hotelling’s tests. In this chapter, we propose a shrinkage-based diagonal Hotelling’s test for both one-sample and two-sample cases. We also propose several different ways to derive the approximate null distribution under different scenarios of p and n for our proposed shrinkage-based test. Simulation studies show that the proposed method performs comparably to existing competitors when n is moderate or large, and it is better when n is small. In addition, we analyze four gene expression data sets and they demonstrate the advantage of our proposed shrinkage-based diagonal Hotelling’s test. Apart from the covariance matrix estimation, we also develop a new classification method for a specific type of high-dimensional data, RNA-sequencing data. In Chapter 5, we propose a negative binomial linear discriminant analysis for RNA-Seq data. By Bayes’ rule, we construct the classifier by fitting a negative binomial model, and propose some plug-in rules to estimate the unknown parameters in the classifier. The relationship between the negative binomial classifier and the Poisson classifier is explored, with a numerical investigation of the impact of dispersion on the discriminant score. Simulation results show the superiority of our proposed method. We also analyze four real RNA-Seq data sets to demonstrate the advantage of our method in real-world applications. Keywords: Covariance matrix, Discriminant analysis, High-dimensional data, Hotelling’s test, Log determinant, RNA-sequencing data.
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Error analysis and tractability for multivariate integration and approximationHuang, Fang-Lun 01 January 2004 (has links)
No description available.
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Power studies of multivariate two-sample tests of comparisonSiluyele, Ian John January 2007 (has links)
Magister Scientiae - MSc / The multivariate two-sample tests provide a means to test the match between two multivariate distributions. Although many tests exist in the literature, relatively little is known about the relative power of these procedures. The studies reported in the thesis contrasts the effectiveness, in terms of power, of seven such tests with a Monte Carlo study. The relative power of the tests was investigated against location, scale, and correlation alternatives. / South Africa
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