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Showing 1 to 20 of 66 for “"Multivariate Normal"”.
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Network Reliability Analysis under the Multivariate Normal Model
High Frequency (HF) radios have been used since the early 20th century for long-distance communication. HF communication systems primarily utilize skywave propagation in which the ionosphere is used to reflect radiowaves back to Earth. The performance of HF communication links is directly tied to …
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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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GLR Control Charts for Monitoring the Mean Vector or the Dispersion of a Multivariate Normal Process
… These quality variables usually follow a multivariate normal (MN) distribution. This 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 …
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Some Results on Classifying an Observation Into One of Several Multivariate Normal Populations With Equal Covariance Matrices
Made available in DSpace on 2014-12-14T14:13:55Z (GMT). No. of bitstreams: 1 8004270.pdf: 5238018 bytes, checksum: 08be13b03839904a4cd1bacf512ac69e (MD5) Previous issue date: 1979
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The applicability of discriminant analysis techniques on the multivariate normal and non-normal data types in marketing research.
The purpose of the procedures described is to assign “objects” or "observations" in some optimum fashion to one of two or more populations. In routine banking a bank manager may wish to classify clients who wish to make loans as low or high credit risks on the basis of the elements of certain …
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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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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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Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets
… and constraints may be observed together as multivariate normal random variates. In the context of general light-tailed distributions, we present the optimal allocation as the result of a concave maximization problem for which the optimal solution is the result of solving one of two nonlinear …
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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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Estimation Of The Scale Matrix Of A Multivariate T-model
The classical theory of Multivariate Statistical Analysis is primarily based on the multivariate normal model. However, in the recent literature several authors have made studies as to how the conclusions will be affected if the population model departs from normality. The class of elliptical …
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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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Statistical Analysis of Longitudinal and Multivariate Discrete Data
<p>Correlated multivariate Poisson and binary variables occur naturally in medical, biological and epidemiological longitudinal studies. Modeling and simulating such variables is difficult because the correlations are restricted by the marginal means via Fréchet bounds in a complicated way. In this …
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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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An empirical evaluation of multivariate sequential procedures for testing means
… this study is to make an empirical evaluation of Multivariate Sequential Procedures for Testing Means as proposed by J. E. Jackson. This was done by simulated sampling from a multivariate normal population with known means, both when the variance-covariance matrix is assumed known (Sequential …
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Selected topics in statistical discriminant analysis.
… approach for multiple high-dimensional multivariate normal populations. Third, we develop a linear dimension reduction method for quadratic discriminant analysis when the class population parameters must be estimated. Using a Monte Carlo simulation with several different parameter …
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Exponentially Weighted Moving Average Charts for Monitoring the Process Generalized Variance
… variance is studied under the independent multivariate normal model for the vector of quality measurements. The performance of the chart is based on an analysis of the chart's initial and steady-state run length distributions. The three methods that are commonly used to determinate run …
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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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