基于时间序列的灰色预测技术在估产模型中的应用

在建立估产模型过程中，引进基于时间序列的灰色预测技术，通过对样本点建立基于时间序列的灰色预测模型和常规的多元线性回归气象模型的分析比较，试图找到一种计算简单，数据要求少而精度较高，时效性较好的建模方法，为时间序列预测在农作物估产方面的应用作出一点探索。     

My Understanding of the Main Similarities and Differences between the Three Translation Models

In this paper, the author wants to prove that the three translation models not only have similarities but also have differences, with the similarities being that they all refer to faithful and free translation and the status of reader, the differences being that their focuses are quite different and their influence upon the present translation theory and practice vary.     

A mixture likelihood approach for generalized linear models

A mixture model approach is developed that simultaneously estimates the posterior membership probabilities of observations to a number of unobservable groups or latent classes, and the parameters of a generalized linear model which relates the observations, distributed according to some member of the exponential family, to a set of specified covariates within each Class. We demonstrate how this approach handles many of the existing latent class regression procedures as special cases, as well as a host of other parametric specifications in the exponential family heretofore not mentioned in the latent class literature. As such we generalize the McCullagh and Nelder approach to a latent class framework. The parameters are estimated using maximum likelihood, and an EM algorithm for estimation is provided. A Monte Carlo study of the performance of the algorithm for several distributions is provided, and the model is illustrated in two empirical applications.     

祁连山地区高分辨率气温降水量分布模型

充分利用气温、降水量空间统计分析的结果，通过在模型中引入坡度（SL）、坡向（SD）变量，对祁连山中东部地区气温、降水量空间变化模式进行尺度下移，得到了该地区具有多尺度特性的高空间分辨率气温、降水量的分析模型：T=a＋bH—kT cos SD sin SL，P=（a＋b ln H＋C ln y）（1＋kp sin SL）；进而计算了该地区7月份和年平均气温的分布式模型和年降水量分布式模型，得到了相应的栅格地图．验证结果表明这两个模型可以与山地冰川和森林分布区的气候分析资料很好地匹配．根据年降水量栅格地图的统计分析，将本区域内的最大降水高度带确定在4500m．     

Bioaccumulation processes in ecosystems

The fate of environmental pollutants — the various isotopes of elements, and inorganic or organic compounds — is a fundamental aspect of ecology and ecotoxicology, and bioaccumulation is a phenomenon often discussed in this context. Human activities have drastically altered natural concentrations of many substances in the environment and added numerous new chemicals. An understanding of the processes of bioaccumulation is important for several reasons. 1) Bioaccumulation in organisms may enhance the persistence of industrial chemicals in the ecosystem as a whole, since they can be fixed in the tissues of organisms. 2) Stored chemicals are not exposed to direct physical, chemical, or biochemical degradation. 3) Stored chemicals can directly affect an individual's health. 4) Predators of those organisms that have bioaccumulated harmful substances may be endangered by food chain effects. While former theories on the processes of bioaccumulation focused on single aspects that affect the extent of accumulation (such as the trophic level within the food chain or the lipophilicity of the chemical), modern theories are based on compartmental kinetics and the integration of various environmental interactions. Concepts include results from quantitative structure-activity relationships (QSAR), pharmacokinetics, ecophysiology and general biology, molecular genetic aspects and selection, and finally the structure of communities and man-made alterations in them.     

Multiple time-series modelling: Another look at the canadian money and income data

The practice of modelling the components of a vector time series to arrive at a joint model for the vector is considered. It is shown that in some cases this is not unreasonable. A vector ARMA model is used to model the Canadian money and income data. We also use these data to discuss the issue of differencing a multiple time series. Finally, models based on first and second differences are compared using forecasts.     

The role of macroeconometric models in forecasting and policy analysis in the united states

This article stresses how little is known about the quality, particularly the relative quality, of macroeconometric models. Most economists make a strict distinction between the quality of a model per se and the accuracy of solutions based on that model. While this distinction is valid, it leaves unanswered how to compare the‘validity’of conditional models. The standard test, the accuracy of ex post simulations, is not definitive when models with differing degrees of exogeneity are compared. In addition, it is extremely difficult to estimate the relative quantitative importance of conceptual problems of models, such as parameter instability across‘policy regimes’ In light of the difficulty in comparisons of conditional macroeconometric models, many model-builders and users assume that the best models are those that have been used to make the most accurate forecasts are those made with the best models. Forecasting experience indicates that forecasters using macroeconometric models have produced more accurate macroeconomic forecasts than either naive or sophisticated unconditional statistical models. It also suggests that judgementally adjusted forecasts have been more accurate than model-based forecasts generated mechanically. The influence of econometrically-based forecasts is now so pervasive that it is difficult to find examples of‘purely judgemental’forecasts.     

非线性贝叶斯动态模型的一种处理方法

对一类非线性贝叶斯动态模型进行了处理。用筛选算法进行抽样，利用得到的样本进行各种推断和预测。     

The impact of parameter and model uncertainty on market risk predictions from GARCH‐type models

We study the effect of parameter and model uncertainty on the left‐tail of predictive densities and in particular on VaR forecasts. To this end, we evaluate the predictive performance of several GARCH‐type models estimated via Bayesian and maximum likelihood techniques. In addition to individual models, several combination methods are considered, such as Bayesian model averaging and (censored) optimal pooling for linear, log or beta linear pools. Daily returns for a set of stock market indexes are predicted over about 13 years from the early 2000s. We find that Bayesian predictive densities improve the VaR backtest at the 1% risk level for single models and for linear and log pools. We also find that the robust VaR backtest exhibited by linear and log pools is better than the backtest of single models at the 5% risk level. Finally, the equally weighted linear pool of Bayesian predictives tends to be the best VaR forecaster in a set of 42 forecasting techniques.