Merging experts’ opinions: A Bayesian hierarchical model with mixture of prior distributions

In this paper, a general approach is proposed to address a full Bayesian analysis for the class of quadratic natural exponential families in the presence of several expert sources of prior information. By expressing the opinion of each expert as a conjugate prior distribution, a mixture model is used by the decision maker to arrive at a consensus of the sources. A hyperprior distribution on the mixing parameters is considered and a procedure based on the expected Kullback–Leibler divergence is proposed to analytically calculate the hyperparameter values. Next, the experts’ prior beliefs are calibrated with respect to the combined posterior belief over the quantity of interest by using expected Kullback–Leibler divergences, which are estimated with a computationally low-cost method. Finally, it is remarkable that the proposed approach can be easily applied in practice, as it is shown with an application.     

An extension of Bar-Hen's preliminary test procedure

An asymptotic test procedure, proposed by Bar-Hen (J. Multivariate Anal. 57 (1996) 266), for deciding if a given set of data represents a new population or one of k a priori known populations, is extended to the case when the new population is described by more than one parameter.     

Estimation of Kullback–Leibler Divergence by Local Likelihood

Motivated from the bandwidth selection problem in local likelihood density estimation and from the problem of assessing a final model chosen by a certain model selection procedure, we consider estimation of the Kullback–Leibler divergence. It is known that the best bandwidth choice for the local likelihood density estimator depends on the distance between the true density and the ‘vehicle’ parametric model. Also, the Kullback–Leibler divergence may be a useful measure based on which one judges how far the true density is away from a parametric family. We propose two estimators of the Kullback-Leibler divergence. We derive their asymptotic distributions and compare finite sample properties. Research of Young Kyung Lee was supported by the Brain Korea 21 Projects in 2004. Byeong U. Park’s research was supported by KOSEF through Statistical Research Center for Complex Systems at Seoul National University.     

Divergences without probability vectors and their applications

In general, divergences and measures of information are defined for probability vectors. However, in some cases, divergences are ‘informally’ used to measure the discrepancy between vectors, which are not necessarily probability vectors. In this paper we examine whether divergences with nonprobability vectors in their arguments share the properties of probabilistic or information theoretic divergences. The results indicate that divergences with nonprobability vectors share, under some conditions, some of the properties of probabilistic or information theoretic divergences and therefore can be considered and used as information measures. We then use these divergences in the problem of actuarial graduation of mortality rates. Copyright © 2009 John Wiley & Sons, Ltd.     

Direct importance estimation for covariate shift adaptation

A situation where training and test samples follow different input distributions is called covariate shift. Under covariate shift, standard learning methods such as maximum likelihood estimation are no longer consistent—weighted variants according to the ratio of test and training input densities are consistent. Therefore, accurately estimating the density ratio, called the importance, is one of the key issues in covariate shift adaptation. A naive approach to this task is to first estimate training and test input densities separately and then estimate the importance by taking the ratio of the estimated densities. However, this naive approach tends to perform poorly since density estimation is a hard task particularly in high dimensional cases. In this paper, we propose a direct importance estimation method that does not involve density estimation. Our method is equipped with a natural cross validation procedure and hence tuning parameters such as the kernel width can be objectively optimized. Furthermore, we give rigorous mathematical proofs for the convergence of the proposed algorithm. Simulations illustrate the usefulness of our approach.     

Stability of the nonlinear filter for slowly switching Markov chains

Exponential stability of the nonlinear filtering equation is revisited, when the signal is a finite state Markov chain. An asymptotic upper bound for the filtering error due to an incorrect initial condition is derived in the case of a slowly switching signal.     

Exponential statistical manifold

We consider the non-parametric statistical model ε(p) of all positive densities q that are connected to a given positive density p by an open exponential arc, i.e. a one-parameter exponential model p(t), t ∈ I, where I is an open interval. On this model there exists a manifold structure modeled on Orlicz spaces, originally introduced in 1995 by Pistone and Sempi. Analytic properties of such a manifold are discussed. Especially, we discuss the regularity of mixture models under this geometry, as such models are related with the notion of e- and m-connections as discussed by Amari and Nagaoka.     

Bayesian prediction based on a class of shrinkage priors for location-scale models

A class of shrinkage priors for multivariate location-scale models is introduced. We consider Bayesian predictive densities for location-scale models and evaluate performance of them using the Kullback–Leibler divergence. We show that Bayesian predictive densities based on priors in the introduced class asymptotically dominate the best invariant predictive density.     

应用RJMCMC方法识别线性模型异常点

传统线性模型异常点识别方法容易发生误判:正常点被归为异常点或者异常点被归为正常点.为解决此类问题,提出了应用逆跳马尔科夫蒙特卡洛方法识别异常点的思想,同时将其应用于实际数据加以检验,识别效果明显好于传统方法.     

稳健稀疏主成分分析法及其实证研究

主成分分析是多元统计分析中一种非常经典的降维技术。然而,经典主成分分析却是对离群值非常敏感的,常因离群值的存在导致结果与实际不相符。另一方面,当主成分分析用于综合评价时,主成分的含义常因载荷间绝对值大小不分明而含糊不清,从而导致综合评价难以展开。本文通过使用稳健稀疏主成分分析法进行模拟实验和实证分析,结果表明:该方法不仅能很好地抵抗离群值的影响,而且还能准确地识别出离群样本。通过该方法得出的主成分的含义也较经典主成分分析和稳健主成分分析更加地明确和贴近实际。     

Improved prediction for a multivariate normal distribution with unknown mean and variance

The prediction problem for a multivariate normal distribution is considered where both mean and variance are unknown. When the Kullback–Leibler loss is used, the Bayesian predictive density based on the right invariant prior, which turns out to be a density of a multivariate t-distribution, is the best invariant and minimax predictive density. In this paper, we introduce an improper shrinkage prior and show that the Bayesian predictive density against the shrinkage prior improves upon the best invariant predictive density when the dimension is greater than or equal to three.     

A general importance sampling algorithm for estimating portfolio loss probabilities in linear factor models

This paper develops a novel importance sampling algorithm for estimating the probability of large portfolio losses in the conditional independence framework. We apply exponential tilts to (i) the distribution of the natural sufficient statistics of the systematic risk factors and (ii) conditional default probabilities, given the simulated values of the systematic risk factors, and select parameter values by minimizing the Kullback–Leibler divergence of the resulting parametric family from the ideal (zero-variance) importance density. Optimal parameter values are shown to satisfy intuitive moment-matching conditions, and the asymptotic behaviour of large portfolios is used to approximate the requisite moments. In a sense we generalize the algorithm of Glasserman and Li (2005) so that it can be applied in a wider variety of models. We show how to implement our algorithm in the $t$ copula model and compare its performance there to the algorithm developed by Chan and Kroese (2010). We find that our algorithm requires substantially less computational time (especially for large portfolios) but is slightly less accurate. Our algorithm can also be used to estimate more general risk measures, such as conditional tail expectations, whereas Chan and Kroese (2010) is specifically designed to estimate loss probabilities.     

On Information Processes in Statistical Experiments with Distributed Observations

Using stochastic integration theory in topological vector spaces general formulas for the Hellinger processes are derived. Feynman–Kac type formulas are obtained for the related Hellinger integrals in terms of the Hellinger processes and the geometric mean measures. The expected logarithmic utility from data, characterized as the Shannon information, is also considered.     

一类基于秩次的稳健线性回归估计与诊断方法

线性回归模型的误差项不服从正态分布或存在多个离群点时,可以将残差秩次的某些函数作为权重引入估计模型来减少离群点的不良影响。本文从参数估计、稳健性质、回归诊断等方面对基于残差秩次的一类稳健回归方法进行介绍.通过模拟研究和实例分析表明,R和GR估计是一种估计效率较高的稳健回归方法,其中GR估计可同时避免X与Y空间离群点,而高失效点HBR估计可通过控制某个参数在稳健性与估计效率之间进行折衷.     

PCA-Fisher判别模型在煤层底板突水预测中的应用

针对当前煤层底板突水影响因素复杂、预测精度低及难度大等问题,通过结合主成分分析法(PCA)和Fisher判别分析法,构建了PCA-Fisher煤层底板突水判别模型,并将该判别模型应用于贵州省六盘水月亮田煤矿9号煤层对其进行底板突水危险性预测.笔者将含水层水压、隔水层厚度及煤层倾角等6个指标作为影响该煤层底板突水危险性的...     

Conditional Iterative Proportional Fitting for Gaussian Distributions

A Gaussian version of the iterative proportional fitting procedure (IFP-P) was applied by Speed and Kiiveri to solve the likelihood equations in graphical Gaussian models. The calculation of the maximum likelihood estimates can be seen as the problem to find a Gaussian distribution with prescribed Gaussian marginals. We extend the Gaussian version of the IPF-P so that additionally given conditionals of Gaussian type are taken into account. The convergence of both proposed procedures, called conditional iterative proportional fitting procedures (CIPF-P), is proved.     

Dimension Reduction in Nonparametric Kernel Discriminant Analysis

This article develops a dimension-reduction method in kernel discriminant analysis, based on a general concept of separation of populations. The ideas we present lead to a characterization of the central subspace that does not impose restrictions on the marginal distribution of the feature vector. We also give a new procedure for estimating relevant directions in the central subspace. Comparisons to other procedures are studied and examples of application are discussed.     

判别分析方法在医学应用中的进展

本文对医学领域中判别分析方法的新进展做一综述,介绍了微阵列基因表达数据判别分析中偏最小二乘法降维、离散小波变换法降维、logitboost算法、随机森林、模糊核判别分析以及时间序列多元数据有序判别分析法、自身有变化规律数据的变系数logistic回归模型判别分析法的基本思想、算法和适用条件。     

A Unified Approach to Outliers,Inuence, and Transformations in Discriminant Analysis

This article extends the analysis of multivariate transformations to linear and quadratic discriminant analysis. It shows that the standard application of deletion diagnostic techniques for validating a particular transformation suffers from masking and so may fail if several outliers are present. We therefore suggest a simple and powerful method which is based on a forward search algorithm. This robust diagnostic procedure orders the observations from those most in agreement with the suggested model to those least in agreement with it. It provides a unified approach to the detection of inuential observations and outliers in discriminant analysis. Simulated and real data are used to show the necessity of considering multivariate transformations in discriminant analysis. The examples demonstrate the power of the suggested approach in revealing the correct structure of the data when this is obscured by outliers.