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Showing 1 to 20 of 27 for “"Multivariate normal distribution"”.
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Certain Minimax-Estimators of the Mean of a Multivariate Normal-Distribution
Made available in DSpace on 2014-12-11T18:24:08Z (GMT). No. of bitstreams: 1 7414519.pdf: 1525731 bytes, checksum: 4f6f5910ae08845bb0e832b5e7304960 (MD5) Previous issue date: 1974
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Analysis of Multivariate Data Using Kotz Type Distribution
… of the inferential statistical methods for multivariate data are developed under the fundamental assumption that the data are from a multivariate normal distribution. Unfortunately, one can never be sure a set of data is really from a multivariate normal distribution. There are numerous …
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Estimating Familial Correlations Using a Kotz Type Density
… likelihood estimators under the assumption of multivariate normality have been extensively studied and compared by various authors. However, the performance of these estimators when the data are not from multivariate normal distribution is poor. In this dissertation, we provide alternative …
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Classification and discriminant analysis
… are presented especially for populations under a normal distribution. Three major techniques of classification and discriminant analysis are presented: linear discriminant analysis, quadratic discriminant procedures and logistic regression. Logistic regression is reviewed in its general framework …
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Bayesian estimation of Thurstonian ranking models based on the Gibbs sampler
… process by latent random variables that follow a multivariate normal distribution. To evaluate the ranking probabilities and estimate the parameters of the ranking models, traditional approaches such as numerical integration methods are only feasible for ranking problems with a small number of …
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Robust MEWMA-type Control Charts for Monitoring the Covariance Matrix of Multivariate Processes
In multivariate statistical process control it is generally assumed that the process variables 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 …
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Applied stochastic Eigen-analysis
… problems when the samples are drawn from a multivariate normal distribution. A longstanding problem in sensor array processing is addressed by designing an estimator for the number of signals in white noise that dramatically outperforms that proposed by Wax and Kailath. This methodology is …
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Prosthesis control using a nearest neighbor electromyographic pattern classifier
… the probability densities were distributed as a multivariate normal distribution. Comparable error rates were obtained with the same data vectors. A condensed nearest neighbor classifier was constructed to determine what minimum number of vectors was necessary in the look-up table. This minimum …
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Multivariate control charts for the mean vector and variance-covariance matrix with variable sampling intervals
… monitor more than one parameter of the process. 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 …
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Multivariate nonparametric control charts using small samples
… involve the assumption that the data follow a multivariate normal distribution. If this assumption is not reasonable or if it is difficult to verify, for example in a short production run, a multivariate control chart based on classical nonparametric statistics could be used. Control charts …
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On wavelet-based testing for serial correlation of unknown form using Fan's adaptive Neyman method
… test is motivated using Fan's (1996) canonical multivariate normal hypothesis testing model. In our framework, the test statistic relies on empirical wavelet coefficients of a wavelet-based spectral density estimator. We advocate the choice of the simple Haar wavelet function, since evidence …
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Applications of Copula Theory and Regime Switching in Finance
… together. Traditional static models, such as the multivariate Normal distribution, are unable to capture these characteristics of the dependence structure, which resulted in copula models attracting attention and becoming popular over the last decade. Copulas provide greater flexibility by …
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Missing data imputation in a clinical registry with deep generative models
… data include simple mean or zero imputation and multivariate imputation that needs a more complex modeling. With the explosion of data and the advancement in the machine learning techniques, more advanced deep generative models have shown the ability to learn complex distributions in high …
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One-sided screening procedure using multiple normally distributed variables
… performance and the screening variables follow a multivariate normal distribution, a regression model is used to predict the value of the performance variable. Using Monte Carlo simulation, the performance of the regression model is evaluated. The evaluation is done for two cases: (1) the …
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Association Study of CHRM2 Polymorphisms with Substance-Use Pathology and Personality Traits
… our results is considerably limited by the non-multivariate-normal distribution of the substance-use pathology variable, incompleteness of the available data, use of self-reported ethnicity instead of genomically-determined ancestry, and sparse coverage of the CHRM2 gene.
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Objective Bayesian Analysis of Kullback-Liebler Divergence of two Multivariate Normal Distributions with Common Covariance Matrix and Star-shape Gaussian Graphical Model
… parts. The second part discusses two population multivariate normal distributions with common covariance matrix. The goal for this part is to derive objective/non-informative priors for the parameterizations and use these priors to build up constructive random posteriors of the Kullback-Liebler …
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Applied stochastic eigen-analysis
… problems when the samples are drawn from a multivariate normal distribution. A longstanding problem in sensor array processing is addressed by designing an estimator for the number of signals in white noise that dramatically outperforms that proposed by Wax and Kailath. This methodology is …
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Structural exploration and inference of the network
… estimation of the inverse covariance matrix of a multivariate normal distribution. An estimator for such models appropriate for multiple graphs analysis in two groups is developed. Under this setting, inferring networks separately ignores the common structure, while inferring networks identically …
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Statistically robust methods for the integration and analysis of X-ray diffraction data from pixel array detectors
… very low background level where assumptions of a normal distribution are no longer valid as an approximation to the Poisson distribution. A second algorithm for modelling the background for each Bragg reflection in a series of X-ray diffraction images containing Debye-Scherrer diffraction from ice …
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Model-based methods for high-dimensional multivariate analysis
… part, we propose a class of estimators of the multivariate response linear regression coefficient matrix that exploits the assumption that the response and predictors have a joint multivariate normal distribution. This allows us to indirectly estimate the regression coefficient matrix through …
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