Estimation of the marginal expected shortfall under asymptotic independence

We study the asymptotic behavior of the marginal expected shortfall when the two random variables are asymptotic independent but positively associated, which is modeled by the so-called tail dependent coefficient. We construct an estimator of the marginal expected shortfall, which is shown to be asymptotically normal. The finite sample performance of the estimator is investigated in a small simulation study. The method is also applied to estimate the expected amount of rainfall at a weather station given that there is a once every 100 years rainfall at another weather station nearby.     

Uniform consistency rate of kNN regression estimation for functional time series data

In this paper, we investigate the k-nearest neighbours (kNN) estimation of nonparametric regression model for strong mixing functional time series data. More precisely, we establish the uniform almost complete convergence rate of the kNN estimator under some mild conditions. Furthermore, a simulation study and an empirical application to the real data analysis of sea surface temperature (SST) are carried out to illustrate the finite sample performances and the usefulness of the kNN approach.     

论阜新经济发展的外向性

文章从市场化的程度、贸易和经济的发展、开放梯度战略以及区域经济布局这四个方面剖析了阜新与经济发达地区的巨大差距及产生差距的原因,阐述了阜新的经济转型、产业结构调整及市场化发育与外向经济的依存关系,特别是从引进项目的基础工作、制定相关优惠政策及提高法律意识等方面,指明了阜新进一步对外开放需要解决和注意的问题。     

Diagnostics for dependence within time series extremes

Summary. The analysis of extreme values within a stationary time series entails various assumptions concerning its long- and short-range dependence. We present a range of new diagnostic tools for assessing whether these assumptions are appropriate and for identifying structure within extreme events. These tools are based on tail characteristics of joint survivor functions but can be implemented by using existing estimation methods for extremes of univariate independent and identically distributed variables. Our diagnostic aids are illustrated through theoretical examples, simulation studies and by application to rainfall and exchange rate data. On the basis of these diagnostics we can explain characteristics that are found in the observed extreme events of these series and also gain insight into the properties of events that are more extreme than those observed.     

Towards Enhancement of the Economy of a Thermal Power Generating System through Prediction of Plant Efficiency

The plant ‘Heat Rate’ (HR) is a measure of overall efficiency of a thermal power generating system. It depends on a large number of factors, some of which are non-measurable, while data relating to others are seldom available and recorded. However, coal quality (expressed in terms of ‘effective heat value’ (EHV) as kcal/kg) transpires to be one of the important factors that influences HR values and data on EHV are available in any thermal power generating system. In the present work, we propose a prediction interval of the HR values on the basis of only EHV, keeping in mind that coal quality is one of the important (but not the only) factors that have a pronounced effect on the combustion process and hence on HR. The underlying theory borrows the idea of providing simultaneous confidence interval (SCI) to the coefficients of a p-th p(≥1) order autoregressive model (AR(p)). The theory has been substantiated with the help of real life data from a power utility (after suitable base and scale transformation of the data to maintain the confidentiality of the classified document). Scope for formulating strategies to enhance the economy of a thermal power generating system has also been explored.     

Local dependence estimation using semiparametric archimedean copulas

The authors define a new semiparametric Archimedean copula family which has a flexible dependence structure. The generator of the family is a local interpolation of existing generators. It has locally‐defined dependence parameters. The authors present a penalized constrained least‐squares method to estimate and smooth these parameters. They illustrate the flexibility of their dependence model in a bi‐variate survival example.     

独立董事“独立性”探讨

独立董事制度的引进,原本是寄期望于此新体制能够完善我国上市公司的公司治理结构。但由于种种原因,这项新制度并没有起到预期的效果,独立董事的"独立性"作用没有得到应有的发挥。我国的独立董事制度建设应具有权威性,赋予给独立董事的权利与其承担的义务应相当。独立董事职责重大,在实践中如何保证他们职责的有效行使是我们应重点考虑的问题,也是独立董事制度能否真正有效的关键。     

A non-profit approach to addressing the effects of urbanization in the Lake Pontchartrain Basin, Louisiana

The Lake Pontchartrain Basin in southeast Louisiana is an estuarine watershed encompassing New Orleans, Baton Rouge, and wetlands that are an integral part of Louisiana's unique culture. In recent decades rapid growth and poorly planned development have resulted in a decrease in water quality and habitat loss, especially in St. Tammany Parish on the north shore of Lake Pontchartrain. In 1989, the Lake Pontchartrain Basin Foundation (LPBF), an environmental, non-profit organization, was formed to address and rectify environmental issues in the Basin. LPBF accomplishes its mission through programs addressing water quality, habitat protection, geographic information science, public access, and education.LPBF used the recent, rapid development occurring in St. Tammany Parish as a case study to examine the relationship between land use, water quality, and development in the Basin by investigating habitat change/urbanization, development in flood plains, and water quality. Through the integration of LPBF programs, it was found that urbanization is having a devastating impact on the parish's natural habitats and severely degrading water quality. Using these results, LPBF is educating Basin citizens on the protection of their habitats and water quality to insure that these resources may be available for future generations.     

A bayesian estimator for the dependence function of a bivariate extreme‐value distribution

Any continuous bivariate distribution can be expressed in terms of its margins and a unique copula. In the case of extreme‐value distributions, the copula is characterized by a dependence function while each margin depends on three parameters. The authors propose a Bayesian approach for the simultaneous estimation of the dependence function and the parameters defining the margins. They describe a nonparametric model for the dependence function and a reversible jump Markov chain Monte Carlo algorithm for the computation of the Bayesian estimator. They show through simulations that their estimator has a smaller mean integrated squared error than classical nonparametric estimators, especially in small samples. They illustrate their approach on a hydrological data set.     

An empirical likelihood goodness-of-fit test for time series

Summary. Standard goodness-of-fit tests for a parametric regression model against a series of nonparametric alternatives are based on residuals arising from a fitted model. When a parametric regression model is compared with a nonparametric model, goodness-of-fit testing can be naturally approached by evaluating the likelihood of the parametric model within a nonparametric framework. We employ the empirical likelihood for an α -mixing process to formulate a test statistic that measures the goodness of fit of a parametric regression model. The technique is based on a comparison with kernel smoothing estimators. The empirical likelihood formulation of the test has two attractive features. One is its automatic consideration of the variation that is associated with the nonparametric fit due to empirical likelihood's ability to Studentize internally. The other is that the asymptotic distribution of the test statistic is free of unknown parameters, avoiding plug-in estimation. We apply the test to a discretized diffusion model which has recently been considered in financial market analysis.