A Spatial-temporal Model for Temperature with Seasonal Variance

We propose a spatial-temporal stochastic model for daily average surface temperature data. First, we build a model for a single spatial location, independently on the spatial information. The model includes trend, seasonality, and mean reversion, together with a seasonally dependent variance of the residuals. The spatial dependency is modelled by a Gaussian random field. Empirical fitting to data collected in 16 measurement stations in Lithuania over more than 40 years shows that our model captures the seasonality in the autocorrelation of the squared residuals, a property of temperature data already observed by other authors. We demonstrate through examples that our spatial-temporal model is applicable for prediction and classification.     

检测系统分辨力定量计算的研究

分辨力是传感器和测量仪器的重要技术参数。数字仪器的分辨力通常取决于内部模数转换器的位数,但如何确定由仪器和传感器组成的检测系统的分辨力,目前尚未见到与之相关的专门性研究成果。该文论证了检测系统的分辨力只与随机误差相关,要提高分辨力就必须减小测量结果的随机波动。通过实验提出了检测系统分辨力的定量计算公式,并证明了其置信概率大于90.9%。     

干线战术通信网的抗毁模型研究

介绍了抗毁通信网分析与设计的原理和方法、针对干线战术通信网中节点移动会导致网络拓扑结构发生变化的特点,提出了动态网络拓扑设计模型和衡量干线战术通信网的抗毁性能的数学模型—随机图模型和齐次随机图模型、计算机模拟结果验证了随机图模型和齐次随机图模型的正确性.     

北京山地森林游憩中游憩者活动特征研究

为更好地促进北京山地森林游憩活动的开展和森林游憩业的可持续发展，本文通过问卷调查对北京市民到山地森林景区游憩的特征进行了研究，统计得到游憩者在森林游憩活动中的出游次数、目的、组织形式、出游季节及时间、对北京周边森林游憩景区和游憩项目喜好等各方面的情况，分析了不同性别、年龄段、职业和收入的游憩者在游憩活动中的差异。最后，在北京森林公园总体布局、游憩区及游憩项目建设、游憩区内风景游憩林培育等方面提出建议。     

A semiparametric method of multiple imputation

In this paper, we describe how to use multiple imputation semiparametrically to obtain estimates of parameters and their standard errors when some individuals have missing data. The methods given require the investigator to know or be able to estimate the process generating the missing data but requires no full distributional form for the data. The method is especially useful for non-standard problems, such as estimating the median when data are missing.     

Clustering and combinatorial optimization in recursive supervised learning

The use of combinations of weak learners to learn a dataset has been shown to be better than the use of a single strong learner. In fact, the idea is so successful that boosting, an algorithm combining several weak learners for supervised learning, has been considered to be the best off the shelf classifier. However, some problems still exist, including determining the optimal number of weak learners and the over fitting of data. In an earlier work, we developed the RPHP algorithm which solves both these problems by using a combination of global search, weak learning and pattern distribution. In this chapter, we revise the global search component by replacing it with a cluster based combinatorial optimization. Patterns are clustered according to the output space of the problem, i.e., natural clusters are formed based on patterns belonging to each class. A combinatorial optimization problem is therefore created, which is solved using evolutionary algorithms. The evolutionary algorithms identify the “easy” and the “difficult” clusters in the system. The removal of the easy patterns then gives way to the focused learning of the more complicated patterns. The problem therefore becomes recursively simpler. Over fitting is overcome by using a set of validation patterns along with a pattern distributor. An algorithm is also proposed to use the pattern distributor to determine the optimal number of recursions and hence the optimal number of weak learners for the problem. Empirical studies show generally good performance when compared to other state of the art methods.     

半平面上有限级随机Dirichlet级数的增长性

证明了右半平面上有限级随机Dirichlet级数的增长性几乎必然与其在每条水平直线上的增长性相同。     

Subsampling Variance Estimation for Non-stationary Spatial Lattice Data

ABSTRACT.  Most proposed subsampling and resampling methods in the literature assume stationary data. In many empirical applications, however, the hypothesis of stationarity can easily be rejected. In this paper, we demonstrate that moment and variance estimators based on the subsampling methodology can also be employed for different types of non-stationarity data. Consistency of estimators are demonstrated under mild moment and mixing conditions. Rates of convergence are provided, giving guidance for the appropriate choice of subshape size. Results from a small simulation study on finite-sample properties are also reported.     

Remedial Policies in Radiologically-Contaminated Forests: Environmental Consequences and Risk Assessment

As a result of the Chernobyl nuclear power plant accident in 1986, large forested areas in Europe were contaminated by radionuclides. Extensive societal pressure has been exerted to decrease the radiation dose to the population and to the environment. Thus, in making abatement and remediation policy decisions not only economic costs, but also human and environmental risk assessment are desired. Forest remediation by organic layer removal, one of the most promising cleanup policies, is considered in this paper. Ecological risk assessment requires evaluation of the radionuclide distribution in forests. The FORESTPATH model^(1,2) is used for predicting the radionuclide fate in forest compartments after deposition as well as for evaluating the application of the remedial policy. Time of intervention and radionuclide deposition profile was predicted as being crucial for the remediation efficiency. Risk assessment conducted for a critical group of forest users in Belarus shows that consumption of forest products (berries and mushrooms) leads to about 0.004% risk of a fatal cancer. Cost-benefit analysis for forest cleanup suggests that complete removal of organic layer is too expensive for application in Belarus.