Gaussian distributions
A regression characterization of inverse Gaussian distributions and application to EDF goodness of fit tests
... inverse Gaussian distributions using the re- gression of a suitable statistic based on a given random ...inverse Gaussian distribution based on a con- ditional joint density function of the ...
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Bayes reliability measures of Lognormal and inverse Gaussian distributions under ML II ε contaminated class of prior distributions
... The numerical illustrations suggest that reasonable amount of misspecification in the prior distribution belonging to the class of ML-II ε-contaminated does not affect the Bayesian reliability measures for lognormal and ...
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Tests of Fit for Normal Variance Inverse Gaussian Distributions
... Abstract —Goodness–of–fit tests for the family of symmetric normal variance inverse Gaussian distri- butions are constructed. The tests are based on a weighted integral incorporating the empirical char- acteristic ...
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Revisit of relationships and models for the Birnbaum-Saunders and inverse-Gaussian distributions
... , s > 0, (7) where α > 0, β > 0 and it is characteristically right-skewed when graphed. As α decreases, particularly for values less than unity, the density becomes nearly symmetric as the curve spread ...
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A reliability application of the mixture of inverse Gaussian distributions
... For the same two sets of initial quality as in Figure 2, a comparison of the mixture's pdf and hazard rate is made with that of a single Inverse Gaussian having a fixed initial condition[r] ...
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What Is the Difference between Gamma and Gaussian Distributions?
... which describes the distance between Gamma and Gau- ssian distributions. The purpose of this paper is to derive asymptotic sharper bound in Equation (5), which much improves the constant by directly using ...
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Analytical properties of generalized Gaussian distributions
... systems with time-hopping (TH) the interference should be modeled with probability distributions that are more impulsive than the Gaussian. Moreover, it has been shown that for the moderate and high ...
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Moments of the generalized hyperbolic distribution
... In this paper we demonstrate a recursive method for obtaining the moments of the gener- alized hyperbolic distribution. The method is readily programmable for numerical evaluation of moments. For low order moments we ...
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Gaussian Mixture Latent Vector Grammars
... present Gaussian Mixture LVeGs (GM-LVeGs), a special case of LVeGs that uses mixtures of Gaussian distributions as the weight functions of fine-grained production ...
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Statistical Analysis and Data Analysis of Stock Market by Interacting Particle Models
... In Figure 9, for d = 2 and λ = 1.01 (around the critical point), we discuss the statistical behavior of the price changes | Δ S | on the parameter θ . In Figure 9(a) and Figure 9(b), since the number of occupied sites is ...
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Assessing misspecification of individual homogeneity assumption in multi-state models based on asymptotic theory
... inverse Gaussian distributions for heterogeneity (individual frailty factor) and studies based on individual homogeneous multi-state models, also confirms the negative bias in the estimation of mean ...
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High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis
... distributions as mixtures of Gaussian distributions or iii) thresholding based on the probability of misclassifi- cation of a healthy pixel followed by a subsequent clus- tering of disea[r] ...
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On some aspects of the asymptotic properties of Bayesian approaches in nonparametric and semiparametric models*
... Beta distributions and nonparametric mixtures of Gaussian distributions lead to adaptive minimax concentration rates over collections of Hölder classes up to a log n term, see [Rousseau, 2010] and ...
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Conical diffraction of a Gaussian beam with a two crystal cascade
... intensity distributions of conically diffracted paraxial light beams [6–8]. These predictions have been shown to agree well with theory for the case of the conically diffracted Gaussian beam [9,10]. The ...
7
Choice of positive distribution law for nuclear data
... truncated Gaussian distribution laws for small relative standard deviations and a lognormal law for larger uncertainty levels ...truncated Gaussian laws can modify the mean and standard deviation ...
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Heavy Tailed Distributions Generated by Randomly Sampled Gaussian, Exponential and Power Law Functions
... y is the continuous derivative of the inverse of y x ( ) . In the following, y x ( ) will be one of the functions (“signal shapes”) to be randomly sampled, i.e. a Gaussian, an exponential or a power-law function ...
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Model based clustering of non Gaussian panel data based on skew t distributions
... We propose a model-based method to cluster units within a panel. The underlying model is autoregressive and non-Gaussian, allowing for both skewness and fat tails, and the units are clustered according to their ...
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Coherent Asset Allocation and Diversification in the Presence of Stress Events
... marginal distributions of sub-portfolio returns cannot be satisfactorily modelled by a Gaussian distribution, or when the portfolio contains strongly non-linear products, then the mapping from the joint ...
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Scale Mixture of Gaussian Modelling of Polarimetric SAR Data
... a Gaussian- like roundness to each dimensional distribution, the second set (location 2) has a reasonably pointed peak with smoothly sloping sides, quite triangular in appearance, and the third set (location 3) ...
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Ellipsoidal Approximation of Distributions and Its Applications
... -algebra of Borel sets is chosen then the finding of is practically impossible. Therefore it is expedient to assume the finite systems of the typical regions as a class of the sets A. It is natural to choose as such ...
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