改进实孔径雷达的方位分辨力

实孔径毫米波FMCW成像雷达系统中方位分辨力与距离分辨力不匹配,影响了成像质量。本文从分析“波束效应”的本质原因出发,提出了一种利用卷积反演—广义逆滤波法,对波束进行“伪压缩”是有效地提高成像雷达方位分辨力的方法。文中给出了对雷达实测数据作“伪压缩”处理的结果,可见处理后的方位分辨力提高约4倍。     

非营利机构服务质量传递系统的过滤模型

非营利机构所提供的产品基本都是无形的服务。而在服务质量传递系统中,有诸多因素影响着服务质量传递的效率,并最终使顾客感知服务质量的效果有所损失。通过引入简化模型和基本函数,建立和求解微分方程,构造出服务质量传递系统的动态模型,并对系统的传递过滤和感知过滤两种效应的效果进行了分析,为非营利机构及其他服务提供者准确衡量和把握顾客心理,实施服务补救,改善服务质量传递的效率和效果,提升非营利机构营销绩效奠定了理论基础。     

Stochastic volatility models for exchange rates and their estimation using quasi-maximum-likelihood methods: an application to the South African Rand

This paper is concerned with the volatility modeling of a set of South African Rand (ZAR) exchange rates. We investigate the quasi-maximum-likelihood (QML) estimator based on the Kalman filter and explore how well a choice of stochastic volatility (SV) models fits the data. We note that a data set from a developing country is used. The main results are: (1) the SV model parameter estimates are in line with those reported from the analysis of high-frequency data for developed countries; (2) the SV models we considered, along with their corresponding QML estimators, fit the data well; (3) using the range return instead of the absolute return as a volatility proxy produces QML estimates that are both less biased and less variable; (4) although the log range of the ZAR exchange rates has a distribution that is quite far from normal, the corresponding QML estimator has a superior performance when compared with the log absolute return.     

Reference Priors for Matrix-Variate Dynamic Linear Models

We develop reference analysis for matrix-variate dynamic models with unknown observation covariance matrices. Bayesian algorithms for forecasting, estimation, and filtering are derived. This work extends the existing theory of reference analysis for univariate dynamic linear models, and thus it proposes a solution to the specification of the prior distributions for a very wide class of time series models. Subclasses of our models include the widely used multivariate and matrix-variate regression models.     

Bayesian Analysis of Stochastic Volatility Models

New techniques for the analysis of stochastic volatility models in which the logarithm of conditional variance follows an autoregressive model are developed. A cyclic Metropolis algorithm is used to construct a Markov-chain simulation tool. Simulations from this Markov chain converge in distribution to draws from the posterior distribution enabling exact finite-sample inference. The exact solution to the filtering/smoothing problem of inferring about the unobserved variance states is a by-product of our Markov-chain method. In addition, multistep-ahead predictive densities can be constructed that reflect both inherent model variability and parameter uncertainty. We illustrate our method by analyzing both daily and weekly data on stock returns and exchange rates. Sampling experiments are conducted to compare the performance of Bayes estimators to method of moments and quasi-maximum likelihood estimators proposed in the literature. In both parameter estimation and filtering, the Bayes estimators outperform these other approaches.     

一种快速确定关键线路的新方法──箭线过滤法

从网络计划关键线路的定义出发,通过对网络计划的结构进行分析,提出了一种快速确定关键线路的新方法──箭线过滤法,并分析了１０种基本图形的过滤方法．     

Continuous-time threshold autoregressions with jumps: Properties,estimation, and application to electricity markets

Continuous-time autoregressive processes have been applied successfully in many fields and are particularly advantageous in the modeling of irregularly spaced or high-frequency time series data. A convenient nonlinear extension of this model are continuous-time threshold autoregressions (CTAR). CTAR allow for greater flexibility in model parameters and can represent a regime switching behavior. However, so far only Gaussian CTAR processes have been defined, so that this model class could not be used for data with jumps, as frequently observed in financial applications. Hence, as a novelty, we construct CTAR processes with jumps in this paper. Existence of a unique weak solution and weak consistency of an Euler approximation scheme is proven. As a closed form expression of the likelihood is not available, we use kernel-based particle filtering for estimation. We fit our model to the Physical Electricity Index and show that it describes the data better than other comparable approaches.