On estimating of the number of constituents of a finite mixture of continuous distributions

Summary Suppose thatH is a mixture of distributions for a given familyF A necessary and sufficient condition is obtained under whichH is, in fact, a finite mixture. An estimator of the number of distributions constituting the mixture is proposed assuming that the mixture is finite and its asymptotic properties are investigated.     

厄兰极值混合模型的有效估计及其在保险中的应用

本文研究了Erlang混合分布和广义帕累托分布混合模型的估计问题.通过引入iSCAD惩罚函数,利用EM算法极大化iSCAD惩罚似然函数的方法,获得了混合序和参数的估计值,计算出有效的度量风险指标value-at-risk(VaR)和tail-VaR(TVaR),通过模拟实验和实际数据说明了模型和算法的有效性.推广了有限Erlang极值混合模型在保险数据拟合中的应用.     

混料对称设计最优性的图检验法

在混料试验中,当混料模型较为复杂且混料成份较多时,要验证一个设计ξ的最优性是比较困难的.一方面,当模型或约束较为复杂时难以证明方差函数是否满足最优性准则条件,另一方面,当混料成份多于3时不能通过绘制方差函数的曲面图来观察最优性.文章提出一种可用于验证混料对称设计的最优性的图检验法,通过实例分析,这种方法是有效的.     

Two New Mixture Models Related to the Inverse Gaussian Distribution

This article presents a new family of logarithmic distributions to be called the sinh mixture inverse Gaussian model and its associated life distribution referred as the extended mixture inverse Gaussian model. Specifically, the density, distribution function, and moments are developed for the sinh mixture inverse Gaussian distribution. Next, the extended mixture inverse Gaussian distribution is characterized. A graphical analysis of the densities of the new models is also provided. In addition, a lifetime analysis is presented for the extended mixture inverse Gaussian distribution. Finally, an example with a real data set is given to illustrate the methodology, which indicates that the new models result in a better fit to the data than some other well-known distributions.     

Merging Mixture Components for Clustering Through Pairwise Overlap

Finite mixture models are well known for their flexibility in modeling heterogeneity in data. Model-based clustering is an important application of mixture models, which assumes that each mixture component distribution can adequately model a particular group of data. Unfortunately, when more than one component is needed for each group, the appealing one-to-one correspondence between mixture components and groups of data is ruined and model-based clustering loses its attractive interpretation. Several remedies have been considered in literature. We discuss the most promising recent results obtained in this area and propose a new algorithm that finds partitionings through merging mixture components relying on their pairwise overlap. The proposed technique is illustrated on a popular classification and several synthetic datasets, with excellent results.     

非饱和土本构关系的混合物理论(Ⅰ)——非线性本构方程和场方程

以混合物理论为基础建立了非饱和土非线性本构方程和场方程.把非饱和土作为3种组分构成的饱和混合物来研究.首先根据土力学成果提出了非饱和土混合物的基本假设,推导出适用于非饱和土混合物的熵不等式;然后采用混合物理论处理本构问题的常规方法得出了非饱和土非线性本构方程;最后把非线性本构方程代入混合物组分动量守恒定律,获得了非饱和土各组分运动的非线性场方程;并且给出了非饱和土混合物的能量守恒方程,从而形成了解决非饱和土混合物热力学过程的完备方程组.     

离子通道的混合密度参数估计与状态判别

本文针对多电导水平离子通道的由多个正态分布加权组成的混合分布特点,用EM迭代算法对混合分布中的参数进行极大似然估计,并在此基础上,利用混合分布中最可能的成分判断通道状态,从而还原通道潜在信号,克服了离子通道分析软件PCLAMP中参数估计与状态还原的缺陷。     

不确定性响应混料模型的最优设计

在混料试验中,响应变量存在着不确定性,提出了一类新的混料试验模型.基于模糊数的可能性方差理论,为该混料模型定义了D-最优和G-最优设计准则.同时,讨论了该模型最优设计的等价定理,并给出实例分析.     

Sampling Bias Correction in the Model of Mixtures with Varying Concentrations

Model of mixture with varying concentrations is a generalization of the classical finite mixture model in which the mixing probabilities (concentrations) vary from observation to observation. We consider the case when the concentrations of the mixture components are known, but no assumptions on the distributions of the observed variable are made. The problem is to estimate the moments of the components’ distributions. We propose a modification of the Horvitz-Thompson weighting for moments estimation by observations from mixture with varying concentrations in presence of sampling bias. Consistency of obtained estimators is demonstrated. Results of simulations are presented.     

Clustering student skill set profiles in a unit hypercube using mixtures of multivariate betas

This paper presents a finite mixture of multivariate betas as a new model-based clustering method tailored to applications where the feature space is constrained to the unit hypercube. The mixture component densities are taken to be conditionally independent, univariate unimodal beta densities (from the subclass of reparameterized beta densities given by Bagnato and Punzo in Comput Stat 28(4):10.1007/s00180-012-367-4, 2013). The EM algorithm used to fit this mixture is discussed in detail, and results from both this beta mixture model and the more standard Gaussian model-based clustering are presented for simulated skill mastery data from a common cognitive diagnosis model and for real data from the Assistment System online mathematics tutor (Feng et al. in J User Model User Adap Inter 19(3):243–266, 2009). The multivariate beta mixture appears to outperform the standard Gaussian model-based clustering approach, as would be expected on the constrained space. Fewer components are selected (by BIC-ICL) in the beta mixture than in the Gaussian mixture, and the resulting clusters seem more reasonable and interpretable.     

A homogeneous gas mixture

We obtain relations for critical values of the mixture of new ideal gases and write differential equations for the mixture of real gases.     

Mixture of Weibull distributions—parametric characterization of failure rate function

A mixture distribution involving two two-parameter Weibull distributions is characterized by five parameters. The shape of the failure rate function for the mixture depends on the parameter values and can be one of eight distinct shapes. In this paper we present a complete parametric characterization of the shapes for the failure rate function for the mixture. © 1998 John Wiley & Sons, Ltd.     

Modeling dependence based on mixture copulas and its application in risk management

This paper is concerned with the statistical modeling of the dependence structure of multivariate financial data using the copula, and the application of copula functions in VaR valuation. After the introduction of the pure copula method and the maximum and minimum mixture copula method, authors present a new algorithm based on the more generalized mixture copula functions and the dependence measure, and apply the method to the portfolio of Shanghai stock composite index and Shenzhen stock component index. Comparing with the results from various methods, one can find that the mixture copula method is better than the pure Gaussian copula method and the maximum and minimum mixture copula method on different VaR level.     

Radial kernels and their reproducing kernel Hilbert spaces

We describe how to use Schoenberg’s theorem for a radial kernel combined with existing bounds on the approximation error functions for Gaussian kernels to obtain a bound on the approximation error function for the radial kernel. The result is applied to the exponential kernel and Student’s kernel. To establish these results we develop a general theory regarding mixtures of kernels. We analyze the reproducing kernel Hilbert space (RKHS) of the mixture in terms of the RKHS’s of the mixture components and prove a type of Jensen inequality between the approximation error function for the mixture and the approximation error functions of the mixture components.     

Stochastic Convexity of the Poisson Mixture Model

This paper is devoted to the study of the compound Poisson mixture model in an actuarial framework. Using the s-convex stochastic orderings and stochastic s-convexity, several problems involving an unknown mixing parameter with given moments are examined; namely, the specification of the number of support points in a finite mixture model, and the derivation of extremal mixture distributions. The theory is enhanced with the derivation of theoretical and numerical bounds on several quantities of actuarial interest.     

A mixture model-based approach to the clustering of exponential repeated data

The analysis of finite mixture models for exponential repeated data is considered. The mixture components correspond to different unknown groups of the statistical units. Dependency and variability of repeated data are taken into account through random effects. For each component, an exponential mixed model is thus defined. When considering parameter estimation in this mixture of exponential mixed models, the EM-algorithm cannot be directly used since the marginal distribution of each mixture component cannot be analytically derived. In this paper, we propose two parameter estimation methods. The first one uses a linearisation specific to the exponential distribution hypothesis within each component. The second approach uses a Metropolis–Hastings algorithm as a building block of a general MCEM-algorithm.     

On homogeneous mixtures of gases

In the paper, relations for the critical values of a mixture of new ideal gases are presented and differential equations for a mixture of real gases are written out.     

Computing Normalizing Constants for Finite Mixture Models via Incremental Mixture Importance Sampling (IMIS)

This article proposes a method for approximating integrated likelihoods in finite mixture models. We formulate the model in terms of the unobserved group memberships, z, and make them the variables of integration. The integral is then evaluated using importance sampling over the z. We propose an adaptive importance sampling function which is itself a mixture, with two types of component distributions, one concentrated and one diffuse. The more concentrated type of component serves the usual purpose of an importance sampling function, sampling mostly group assignments of high posterior probability. The less concentrated type of component allows for the importance sampling function to explore the space in a controlled way to find other, unvisited assignments with high posterior probability. Components are added adaptively, one at a time, to cover areas of high posterior probability not well covered by the current importance sampling function. The method is called incremental mixture importance sampling (IMIS).

IMIS is easy to implement and to monitor for convergence. It scales easily for higher dimensional mixture distributions when a conjugate prior is specified for the mixture parameters. The simulated values on which the estimate is based are independent, which allows for straightforward estimation of standard errors. The self-monitoring aspects of the method make it easier to adjust tuning parameters in the course of estimation than standard Markov chain Monte Carlo algorithms. With only small modifications to the code, one can use the method for a wide variety of mixture distributions of different dimensions. The method performed well in simulations and in mixture problems in astronomy and medical research.     

Flexible Multivariate Density Estimation With Marginal Adaptation

This article is concerned with multivariate density estimation. We discuss deficiencies in two popular multivariate density estimators—mixture and copula estimators, and propose a new class of estimators that combines the advantages of both mixture and copula modeling, while being more robust to their weaknesses. Our method adapts any multivariate density estimator using information obtained by separately estimating the marginals. We propose two marginally adapted estimators based on a multivariate mixture of normals and a mixture of factor analyzers estimators. These estimators are implemented using computationally efficient split-and-elimination variational Bayes algorithms. It is shown through simulation and real-data examples that the marginally adapted estimators are capable of improving on their original estimators and compare favorably with other existing methods. Supplementary materials for this article are available online.     

Mixture model modal clustering

Advances in Data Analysis and Classification - The two most extended density-based approaches to clustering are surely mixture model clustering and modal clustering. In the mixture model approach,...