Normal Mixture
Normal mixture quasi maximum likelihood estimation for non-stationary TGARCH(1,1) models
... Abstract: Although quasi maximum likelihood estimator based on Gaussian density (G-QMLE) is widely used to estimate GARCH-type models, it does not perform successfully when error distribution is either skewed or ...
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The Predictive Performance of Asymmetric Normal Mixture GARCH in Risk Management: Evidence from Turkey
... (asymmetric) normal mixture GARCH process lies in the fact that it can captures tails in the financial time series more ...The normal mixture GARCH models are similar to Markov switching ...
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Effective Truncation of a Student’s t Distribution by Truncation of the Chi Distribution in a Chi Normal Mixture
... chi-normal mixture is investigated and expressions for the pdf, the variance, and the kurtosis of the t-like distribu- tion that arises from the mixture of a left-truncated chi and a normal ...
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Statistical approach on grading the student achievement via normal mixture modeling
... the Normal Mixture distribution where the grades are distinctively separated by the parameters: means and proportions of the Normal Mixture ...
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Asymmetric Laplace-Normal Mixture Model for Wind Shear Data
... A mixture model with Laplace and Normal distributions fitted to the data obtained from a civil airline is ...above mixture model with asymmetric Laplace distribution, a skewed version of symmetric ...
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Maximum Likelihood Estimation of the Multivariate Normal Mixture Model
... (multivariate) mixture models in terms of the inverse of the observed information matrix, and they differ by the way this inverse is ...a mixture component is an unobserved variable coming from a ...
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Null distribution of multiple correlation coefficient under mixture normal model
... In practice, it is often the case that the random variables are not normally dis- tributed. When such is the case, how would the departure from the normality affect the conventional inference procedure? Specifically, one ...
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A Study of Log-concave Mixture Models.
... This paper proposed two robust EM-type algorithms for the log-concave mixtures of regression models. These algorithms provide more flexibility, which allows a large family of error densities in the mixtures of regression ...
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A Quantitative Trait Locus Mixture Model That Avoids Spurious LOD Score Peaks
... a normal mixture ...the mixture model compared to a single normal distribution (the LOD ...a mixture of normals always produces a better fit than a single normal ...a ...
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Density forecasting of the Dow Jones share index
... the normal (N), normal mixture (NM) and a weighted sum of a normal and an As- symetric Laplace distribution ...mixed Normal- Asymmetric Laplace distribution, Method of Moments ...
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Fitting Finite Mixtures of Generalized Linear Regressions on Motor Insurance Claims
... best mixture model for claim amount from a comprehensive insurance policy portfolio and use the model to estimate the expected claim amount per risk for the coming calendar ...four mixture models namely the ...
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Model based clustering using copulas with applications
... copula-based mixture model to that of a Normal mixture fitted using the mclust (Fra- ley et ...the Normal mixture model with the lowest BIC (4 components with VEV parameterization with ...
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Compound Normal with Gamma Mixture Models A Model Characteristic Perspective
... compound normal with gamma distribution and its truncated version is computed using Equation (7), which defines the variance ( ) as a function of scale(c) and shape(v) ...of normal mixture, compound ...
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RЀNYI ENTROPY FOR MIXTURE MODEL OF ULTIVARIATE SKEW NORMAL CAUCHY DISTRIBUTIONS
... The GM-PHD filter is a multi-object tracking algorithm that probabilistically determines all object-observation order pairs that can be considered and solves this problem without additional algorithms. In other words, ...
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Fluorescence-Assisted Cytological Testing (FACT): Ex Vivo Viral Method for Enhancing Detection of Rare Cancer Cells in Body Fluids
... Given these limitations of standard cytopathological tests, several investiga- tors have recently proposed automated techniques for detection of cancer cells (18–21). These techniques exploit the differences in cellular ...
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RЀNYI ENTROPY FOR MIXTURE MODEL OF ULTIVARIATE SKEW NORMAL CAUCHY DISTRIBUTIONS
... We have derived the upper and lower bounds on the Rѐnyi entropy of X~MMSCN 0, S, δ, ε . Using the mean of these bounds, the approximate values of entropy can be calculated. These values are localized between bounds of ...
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RЀNYI ENTROPY FOR MIXTURE MODEL OF ULTIVARIATE SKEW NORMAL CAUCHY DISTRIBUTIONS
... supports the claim that the computational-RDT model can reason in the sense that in the simulation environment when the driver is exposed to longterm training, it can easily be identifie[r] ...
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Alternative prior distributions for variable selection with very many more variables than observations
... the normal-exponential- gamma (NEG), which allows a spike at zero which is not infinite and is proper over its full ...the normal-Jeffreys with weak shrinkage for large coefficients without its evident ...
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Linking the Value Assessment of Oil and Gas Firms to Ambidexterity Theory Using a Mixture of Normal Distributions
... tailed” mixture of two normal distributions offers a significantly more accurate model than the traditionally Gaussian approach in describing the behavior of the value of oil and gas fi ...This mixture ...
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On the Bayesian analysis of species sampling mixture models for density estimation
... 82 galaxies are moving away from our galaxy. Some galaxies are thought to be moving at similar speeds whilst other move much faster or slower. Inferring the clusters of galaxy is the main inferential problem. Of course, ...
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