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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 27 for “"Mixture Distribution"”.
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Effect of Fuel Volatility on Mixture Distribution in a v-8 Automotive Engine
Made available in DSpace on 2015-05-12T17:06:22Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 0023375.PDF: 8533081 bytes, checksum: 8269993b9c4f67f90449bc2a60c9bb21 (MD5) Previous issue date: 1957
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Modelling Locally Changing Variance Structured Time Series Data By Using Breakpoints Bootstrap Filtering
… specifying any prior knowledge of the underlying distribution function of the time series. The effect of covariates is controlled by fitting the linear regression model with serially correlated errors. In the second stage, we partition the time series into consecutive non-overlapping intervals of …
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A Normal-Mixture Model with Random-Effects for RR-Interval Data
… as heart rate variability (HRV) data, a normal-mixture distribution seems to be more appropriate than the normal distribution assumption. While the random-effects methodology is well developed for several distributions in the exponential family, the case of the normal-mixture has not been dealt …
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A new approach to calculate and forecast dynamic conditional correlation - the use of a multivariate heteroskedastic mixture model
… new conditional heteroscedastic models based on mixture techniques. Specifically, Engle’s standard DCC is augmented with an asymmetric factor and then modified so that disturbances (conditional returns) can be modelled using multivariate Gaussian mixture distribution and multivariate T mixture …
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Modeling Distributions of Test Scores with Mixtures of Beta Distributions
Test score distributions are used to make important instructional decisions about students. The test scores usually do not follow a normal distribution. In some cases, the scores appear to follow a bimodal distribution that can be modeled with a mixture of beta distributions. This bimodality may be …
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Modelling examples of loss given default and probability of default
… Recovery Amount, so as to predict LGD. Secondly, mixture distribution models are developed based on linear regression and survival analysis approaches. A comparison between single distribution models and mixture distribution models is made and their advantages and disadvantages are …
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Least squares mixture decomposition estimation
The Least Squares Mixture Decomposition Estimator (LSMDE) is a new nonparametric density estimation technique developed by modifying the ordinary kernel density estimators. While the ordinary kernel density estimator assumes equal weight (l/<i>n</i>) for each data point, LSMDE assigns the optimized …
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Bayesian analysis of finite mixture distributions using the allocation sampler
Finite mixture distributions are receiving more and more attention from statisticians in many different fields of research because they are a very flexible class of models. They are typically used for density estimation or to model population heterogeneity. One can think of a finite mixture …
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Predictive reliabilities for electronic components
… state k from initial state l. First passage time distribution is derived for different forms of transition rates. When the initial and final states of the process are considered as random, the failure time is expressed as the mixture distribution obtained from the conditional first passage time …
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Some applications of generalised linear models
… that grouped data arising from a truncated or mixture distribution can be represented as a parametric composite link function and the technique applied to extend the analysis of some previously published data sets. Following a transformation, it is shown that certain time series models may …
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Fault Detection for Systems with Multiple Unknown Modes and Similar Units
… developed based on estimating a common Gaussian-mixture distribution for unit parameters whereby observations are mapped into a common parameter-space and clusters are then identified corresponding to different modes of operation via the Expectation- Maximization algorithm. The estimated common …
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Degradation Analysis for Heterogeneous Data Using Mixture Model
… environmental condition, etc. The normal distribution may not be adequate to describe the observed unit-to-unit variability. Reliability analysis for units from a nonhomogeneous population with subgroups has been considered only in failure time analysis.This thesis considers the …
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Extended Poisson Models for Count Data With Inflated Frequencies
… (2011), Lin and Tsai (2012) introduced a mixture model to account for the inflated frequencies of zero and <em>k</em>. In this dissertation, we study basic properties of this mixture model and parameter estimation for grouped and ungrouped data. Using stochastic representation we show how …
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Normal Mixture Models for Gene Cluster Identification in Two Dimensional Microarray Data
… A novel clustering technique based on normal mixture distribution models is developed. This method clusters observations that arise from the same normal distribution and allows the data to be simultaneously clustered in two dimensions. The model is fitted using the Expectation/Maximization …
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Molecular Code Division Multiple Access: Gaussian Mixture Modeling
… molecular signal is modeled as a Gaussian mixture distribution when the MC system undergoes Brownian noise and inter-symbol interference (ISI). This novel approach demonstrates a suitable modeling for diffusion-based MC system. Using the proposed Gaussian mixture model, a simple receiver is …
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Novel Statistical Methodologies in Analysis of Position Emission Tomography Data: Applications in Segmentation, Normalization, and Trajectory Modeling
… of functional data analysis to image intensity distribution functions, assuming that that individual image density functions are variations from a template density. By modeling the warping functions using a modified function-on-scalar regression, the variations in density functions due to …
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Circular Probabilistic Based Color Processing: Applications in Digital Pathology Image Analysis
… of hue, the study innovates to model a hue distribution of an image using a circular mixture distribution, and provides a complete hue-based pixel clustering solution through maximum likelihood estimation. The second method aims to address the singularity of the HSV space in color …
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Statistical process control by quantile approach.
… procedures involve making assumptions about the distributional form of data it uses; usually that the data is normally distributed. It is common place to find processes that generate data which is non-normally distributed, e.g. Weibull, logistic or mixture data is increasingly encountered. Any …
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Principal component analysis and classification of discrete and mixed feature datasets using Gaussian copula
… algorithm that samples from the truncated normal distribution, which is also used in classification problem for calculating the posterior probability. In Chapter 3, a classification model based on the mixture of discrete Gaussian copula family distributions is proposed. The optimal classification …
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Signal Detection and Modulation Classification in Non-Gaussian Noise Environments
… assume that the additive noise has a Gaussian distribution. However, while this is a good model for thermal noise, various studies have shown that the noise experienced in most radio channels, due to a variety of man-made and natural electromagnetic sources, is non-Gaussian and exhibits …
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