Graphical Models for Composable Finite Markov Processes

Abstract.  Composable Markov processes were introduced by Schweder (1970) in order to capture the idea that a process can be composed of different components where some of these only depend on a subset of the other components. Here we propose a graphical representation of this kind of dependence which has been called 'local dependence'. It is shown that the graph allows to read off further independencies characterizing the underlying Markov process. Also, some standard methods for inference are adapted to exploit the graphical representation, e.g. for testing local independence.     

COVARIANCE MODELS FOR LATTICE DATA

Given observations on an m × n lattice, approximate maximum likelihood estimates are derived for a family of models including direct covariance, spatial moving average, conditional autoregressive and simultaneous autoregressive models. The approach involves expressing the (approximate) covariance matrix of the observed variables in terms of a linear combination of neighbour relationship matrices, raised to a power. The structure is such that the eigenvectors of the covariance matrix are independent of the parameters of interest. This result leads to a simple Fisher scoring type algorithm for estimating the parameters. The ideas are illustrated by fitting models to some remotely sensed data.     

供应突发事件下基于CVaR的供应链订货决策及协调

供应突发事件下,引入条件风险值(conditional value at risk-CVa R)刻画了零售商的运营目标,构建了收益共享契约下的供应链订货模型,着重研究了CVa R下的供应链协调及零售商最优订货量对供应商可靠性及对其自身的风险规避系数的敏感性。研究表明:收益共享契约具有一定的鲁棒性,能协调突发事件风险下的供应链;风险规避型零售商的最优订货量总是不小于风险中性情况,且风险规避程度越高,订货量越大;最优订货量对供应商可靠性均值的敏感性不依赖于零售商的风险规避程度,且均值越小,最优订货量越大,这与风险中性情况是类似的;最优订货量对供应商可靠性标准差的敏感性则依赖于零售商的风险规避程度,当零售商的风险规避程度较高时,供应可靠性标准差越大,最优订货量越大,这与风险中性情况是相反的。     

副词“才”主观量表达的语义基础

副词“才”表示“方始”,即在某个时间点,某个事件从无到有。强调事件在这个时间点而不是这个时间点之前实现,并且,从事件实现到说话时的时间段相对较短。当客观时间量在句子中出现,“才”进一步虚化表达主观量:前指表达主观大量,后指表达主观小量。“T才P”是限制条件的肯定P,这个条件就是T,用于肯定句可以用转折来表示,而条件之外则不能肯定P。名词语进入“才 名词语”这一句法格式的条件是:名词本身有明显的序列推移性或者在某种特定条件下能进入某个排序中。     

Non-parametric estimation of the conditional mode

The problem of estimating the mode of a conditional probability density function is considered. It is shown that under some regularity conditions the estimate of the conditional mode obtained by maximizing a kernel estimate of the conditional probability density function is strongly consistent and asymptotically normally distributed.     

The Gaussian Mixture Dynamic Conditional Correlation Model: Parameter Estimation,Value at Risk Calculation,and Portfolio Selection

A multivariate generalized autoregressive conditional heteroscedasticity model with dynamic conditional correlations is proposed, in which the individual conditional volatilities follow exponential generalized autoregressive conditional heteroscedasticity models and the standardized innovations follow a mixture of Gaussian distributions. Inference on the model parameters and prediction of future volatilities are addressed by both maximum likelihood and Bayesian estimation methods. Estimation of the Value at Risk of a given portfolio and selection of optimal portfolios under the proposed specification are addressed. The good performance of the proposed methodology is illustrated via Monte Carlo experiments and the analysis of the daily closing prices of the Dow Jones and NASDAQ indexes.     

Out-of-Sample Performance of Discrete-Time Spot Interest Rate Models

We provide a comprehensive analysis of the out-of-sample performance of a wide variety of spot rate models in forecasting the probability density of future interest rates. Although the most parsimonious models perform best in forecasting the conditional mean of many financial time series, we find that the spot rate models that incorporate conditional heteroscedasticity and excess kurtosis or heavy tails have better density forecasts. Generalized autoregressive conditional heteroscedasticity significantly improves the modeling of the conditional variance and kurtosis, whereas regime switching and jumps improve the modeling of the marginal density of interest rates. Our analysis shows that the sophisticated spot rate models in the existing literature are important for applications involving density forecasts of interest rates.     

Influence analysis in multivariate linear general models

In this paper we obtain several influence measures for the multivariate linear general model through the approach proposed by Muñoz-Pichardo et al. (1995), which is based on the concept of conditional bias. An interesting charasteristic of this approach is that it does not require any distributional hypothesis. Appling the obtained results to the multivariate regression model, we obtain some measures proposed by other authors. Nevertheless, on the results obtained in this paper, we emphasize two aspects. First, they provide a theoretical foundation for measures proposed by other authors for the mul¬tivariate regression model. Second, they can be applied to any linear model that can be formulated as a particular case of the multivariate linear general model. In particular, we carry out an application to the multivariate analysis of covariance.     

An efficient non-rejective implementation of the πps sampling designs

Poisson sampling is a method for unequal probabilities sampling with random sample size. There exist several implementations of the Poisson sampling design, with fixed sample size, which almost all are rejective methods, that is, the sample is not always accepted. Thus, the existing methods can be time-consuming or even infeasible in some situations. In this paper, a fast and non-rejective method, which is efficient even for large populations, is proposed and studied. The method is a new design for selecting a sample of fixed size with unequal inclusion probabilities. For the population of large size, the proposed design is very close to the strict πps sampling which is similar to the conditional Poisson (CP) sampling design, but the implementation of the design is much more efficient than the CP sampling. And the inclusion probabilities can be calculated recursively.     

Bayesian classification with multivariate autoregressive sources that might have different orders

The main objective of this paper is to develop an exact Bayesian technique that can be used to assign a multivariate time series realization to one of several autoregressive sources, with unknown coefficients and precision, that might have different orders. The foundation of the proposed technique is to develop the posterior mass function of a classification vector, in an easy form, using the conditional likelihood function. A multivariate time series realization is assigned to the multivariate autoregressive source with the largest posterior probability. A simulation study, with uniform prior mass function, is carried out to demonstrate the performance of the proposed technique and to test its adequacy in handling the multivariate classification problems. The analysis of the numerical results supports the adequacy of the proposed technique in solving the classification problems with multivariate autoregressive sources.