多维和多重递推辅助变量辨识算法

对带白色观测噪声的多维自回归(AR)模型,应用相关方法,分别提出了多维和多重递推辅助变量(RIV)算法,给出了未知AR参数的强一致估计;推广了一维RIV算法。提出了模型噪声和观测噪声方差的信息融合估值器,它们具有强一致性。一个仿真例子说明其有效性。     

An aggregate sales model for consumer durables incorporating a time‐varying mean replacement age

Forecasting category or industry sales is a vital component of a company's planning and control activities. Sales for most mature durable product categories are dominated by replacement purchases. Previous sales models which explicitly incorporate a component of sales due to replacement assume there is an age distribution for replacements of existing units which remains constant over time. However, there is evidence that changes in factors such as product reliability/durability, price, repair costs, scrapping values, styling and economic conditions will result in changes in the mean replacement age of units. This paper develops a model for such time‐varying replacement behaviour and empirically tests it in the Australian automotive industry. Both longitudinal census data and the empirical analysis of the replacement sales model confirm that there has been a substantial increase in the average aggregate replacement age for motor vehicles over the past 20 years. Further, much of this variation could be explained by real price increases and a linear temporal trend. Consequently, the time‐varying model significantly outperformed previous models both in terms of fitting and forecasting the sales data. Copyright © 2001 John Wiley & Sons, Ltd.     

Alternative regime switching models for forecasting inflation

US inflation appears to undergo shifts in its mean level and variability. We evaluate the performance of three useful models for capturing such shifts. The models studied are the Markov switching models, state space models with heavy‐tailed errors, and state space models with compound error distributions. Our study shows that all three models have very similar performance when evaluated in terms of the mean squared or mean absolute forecast errors. However, the latter two models are considerably more parsimonious, and easily beat the more profligately parameterized Markov switching models in terms of model selection criteria, such as the AIC or the SBC. Thus, these may serve as useful continuous alternatives to the popular discrete Markov switching models for capturing shifts in time series. Copyright © 2001 John Wiley & Sons, Ltd.     

DLR-F6翼身组合体跨音速流场

针对跨音速运输机经典算例DLR-F6翼身组合体模型,采用CFD方法对其气动特性进行了粘性流动数值模拟,流动模型为雷诺平均NS(RANS)方程。首先采用“超立方体”概念生成绕DLR-F6翼身组合体的高质量多块结构拼接网格,研究网格拓扑结构对气动特性的影响,在此基础上通过网格细分和粗分考查了网格密度对计算结果的影响,最后进行了湍流模型的影响研究。通过与实验数据对比分析,得出了适宜DLR-F6翼身组合体跨音速粘性流动的计算网格,并总结出了能较好模拟其跨音速流场特性的湍流模型。结果表明：网格拓扑结构的合理设计会对计算结果产生一定的影响;网格密度对机翼表面压力分布没有明显影响,但对阻力系数影响显著;湍流模型对机翼表面压力系数分布的影响主要体现在激波位置上,对翼根处的分离也有一定的影响;SST 模型计算的气动力系数比SA模型接近实验值。     

Neural network versus econometric models in forecasting inflation

Artificial neural network modelling has recently attracted much attention as a new technique for estimation and forecasting in economics and finance. The chief advantages of this new approach are that such models can usually find a solution for very complex problems, and that they are free from the assumption of linearity that is often adopted to make the traditional methods tractable. In this paper we compare the performance of Back‐Propagation Artificial Neural Network (BPN) models with the traditional econometric approaches to forecasting the inflation rate. Of the traditional econometric models we use a structural reduced‐form model, an ARIMA model, a vector autoregressive model, and a Bayesian vector autoregression model. We compare each econometric model with a hybrid BPN model which uses the same set of variables. Dynamic forecasts are compared for three different horizons: one, three and twelve months ahead. Root mean squared errors and mean absolute errors are used to compare quality of forecasts. The results show the hybrid BPN models are able to forecast as well as all the traditional econometric methods, and to outperform them in some cases. Copyright © 2000 John Wiley & Sons, Ltd.     

Neural networks and the multinomial logit for brand choice modelling: a hybrid approach

The study of brand choice decisions with multiple alternatives has been successfully modelled for more than a decade using the Multinomial Logit model. Recently, neural network modelling has received increasing attention and has been applied to an array of marketing problems such as market response or segmentation. We show that a Feedforward Neural Network with Softmax output units and shared weights can be viewed as a generalization of the Multinomial Logit model. The main difference between the two approaches lies in the ability of neural networks to model non‐linear preferences with few (if any) a priori assumptions about the nature of the underlying utility function, while the Multinomial Logit can suffer from a specification bias. Being complementary, these approaches are combined into a single framework. The neural network is used as a diagnostic and specification tool for the Logit model, which will provide interpretable coefficients and significance statistics. The method is illustrated on an artificial dataset where the market is heterogeneous. We then apply the approach to panel scanner data of purchase records, using the Logit to analyse the non‐linearities detected by the neural network. Copyright © 2000 John Wiley & Sons, Ltd.     

影响图在非线性回归异方差检验中的应用

提出把加权非线性回归模型看作一个扰动模型,利用影响图的正则曲率研究了此模型的异方差检验问题,并在此基础上得到了改进的Score统计量。最后用具体的数值实例说明所得结论的有效性。     

交换Fuzzy正则语言注记

在探讨形式语言的交换性质的基础上,提出了AbelianFuzzy正则语言的概念,探讨了AbelianFuzzy正则语言在并、交和补算子下的封闭性,并得到了Fuzzy正则语言是半交换的的几个等价条件.由于Fuzzy正则语言的交换性质是形式语言之中一种较好的性质,对它的研究将有助于Fuzzy正则语言的机器实现.     

量子语言的一些性质

给出了不同输入字符集上两个q 自动机张量积的定义,研究了不同输入字符集上两个q 自动机的张量积识别的语言.同时讨论了η 量子语言和广义η 量子语言,给出了η 量子语言和广义η 量子语言的一些性质.     

A Bayesian Approach to Modelling the Observed Bovine Spongiform Encephalopathy Epidemic

The threat of Bovine Spongiform Encephalopathy (BSE) to humans was realized in March 1996, when the Spongiform Encephalopathy Advisory Committee (SEAC) reported to the government that the most likely explanation of the new variant Creutsfeldt–Jakob Disease (nvCJD) in humans was exposure to the BSE agent. To assess the potential risk of nvCJD, it is necessary to understand the development of the BSE epidemic within the cattle population. This paper reports on work undertaken to model the observed BSE epidemic in the UK using established Bayesian methodology. © 1997 John Wiley & Sons, Ltd.