基于SAR遥感的北方旱地秋收作物识别研究

在我国北方旱地秋收作物生长关键期,云雨天气影响较大,无法及时、有效地获取光学遥感数据,因此利用雷达遥感进行旱地作物识别研究非常必要。文章以河北省衡水市为研究区,选择6期RADARAST-2全极化影像作为数据源,分类方法为随机森林法。首先通过对比不同时相间的组合结果,优选出了研究区典型秋收作物(玉米、棉花)的最佳识别时相及组合方式。其次,提取最优识别时相的后向散射信息、纹理信息、极化分解等3部分信息,依据信息间相互组合的结果及随机森林算法对变量的重要性评价,文中对上述3部分信息进行了重要性评估。结果表明:利用SAR识别旱地秋收作物时应着重关注作物生长前期的时相,其中玉米在6月27日单一时相下就可获得90%以上的高精度;棉花面积小、地块破碎,但通过6月3日与6月27日两个时相的结合也得到了70%以上的精度。在玉米识别中极化信息具有较大的贡献,极化变量的加入主要增加了玉米和建筑用地的可分离性,与单纯利用后向散射信息分类相比精度提高了近7%;同样,纹理信息和极化分解信息的加入也使棉花的精度提高了3%。最后,利用随机森林算法对变量的重要性评价机制,优选出对玉米识别最为重要的5个变量,依次为:VH、Alpha、Yamaguchi4-Odd、Freeman-Vol和Mean(HV)。该研究利用雷达数据进行旱地作物识别,验证了雷达影像对旱地秋收作物的识别能力,不仅保证了数据获取与天气状况的独立性,还凭借SAR独有的数据获取方式,为光学数据提供了补充。     

基于价值工程方法改进的多维Logit模型研究

通过引入价值工程方法,改进基于随机效用理论的多维Logit模型,形成VEL模型,对VEL模型进行了功能分析和广义出行成本分析,最后论述出行人群的划分方式和集计化。     

供应链优化管理:关于量折扣研究的分类

供应链管理中的量折扣研究一直是研究的热点问题之一。国外学者研究的重点在于解决整体供应链的有效协调性问题,而其中量折扣定价策略研究是促进供应链协调运作的有效手段。这些研究的大致可分为三类:(1)给定需求条件下研究如何协调生产及库存决策;(2)从营销的角度研究供应链的协调;(3)将第一类研究和第二类研究相结结合,同时考虑市场营销和生产库存协调运作。而国内学者虽然引入了一些新方法,但仍处于从第一类研究向第二、三类研究的发展阶段。     

基于ADASYN和WGAN的混合不平衡数据处理方法

为了解决不平衡数据集中少数类样本分类精度较低的问题,提出了一种处理不平衡数据集的ADASYN-WGAN方法。首先,采用ADASYN(adaptive synthetic sampling)算法生成少数类样本,用这些生成样本代替WGAN(wasserstein generative adversarial networks)中的随机噪声;其次,利用WGAN算法生成符合原始数据集分布规律的少数类样本,构建平衡数据集;然后,在6个公开数据集上,采用随机森林分类器对所提方法和4种过采样算法得出的处理结果分别与原始数据集进行对比;最后,通过F1-Score,G-mean和AUC等分类评估指标的表现验证所提方法的有效性。结果表明：在对比实验中,经过ADASYN-WGAN方法得到的平衡数据集在随机森林分类器的十折交叉验证中,4个公开数据集中的各项分类评估指标值均达到最优,虽然另2个公开数据集中的AUC值略低,但其F1-Score和G-mean取得了最高值。所提出的ADASYN-WGAN方法可生成高质量的数据样本,并可为解决不平衡数据集中少数类样本的预测偏差问题提供参考。     

Estimation in Surveys Using Conditional Inclusion Probabilities: Simple Random Sampling

In survey sampling, auxiliary information on the population is often available. The aim of this paper is to develop a method which allows one to take into account such auxiliary information at the estimation stage by means of conditional bias adjustment. The basic idea is to attempt to construct a conditionally unbiased estimator. Four estimators that have a small conditional bias with respect to a statistic are proposed. It is shown that many of the estimators used in the literature in the case of simple random sampling can be obtained by using this estimation principle. The problem of simple random sampling with replacement, poststratification, and adjustment of a 2 x 2 dimensional contingency table to marginal totals are discussed in the conditional framework. Finally it is shown that the regression estimator can be viewed as an approximation of an application of the conditional principle.     

Real indeterminacy of equilibria and manipulability

In this paper, we analyze the indeterminacy of equilibria in financial markets and propose a selection mechanism. We suggest that there is one equilibrium that prevails over the others, as a result of the market power of the agents that some states of nature become monopolists of certain commodities. Given a financial assets model, we define a price game and show the existence of mixed strategies equilibria. Then we purify these equilibria by considering a price game with incomplete information.     

Stochastic ordering and schur-convex functions in comparison of linear experiments

The usual ordering of linear experiments is defined by quadratic risk of attainable linear estimators. It is shown that under normality assumption this ordering can be introduced in a risk-free way by stochastic ordering of the estimators. Moreover an application of Schur-convex functions to design of experiments is presented. Partly supported by CPBP 0.1.02.     

Comparing mean efficiency and productivity scores from small samples: A bootstrap methodology

This paper provides a bootstrap methodology for constructing confidence intervals for means of DEA and econometrically estimated efficiency scores, Malmquist productivity indices, and other similar measures in small samples. The procedure is nonparametric since no distributional assumptions are required. An empirical example is provided.This research was performed while Wilson was under contract with the Management Science Group, US Department of Veterans Affairs, Bedford, MA 01730.     

MEAN VARIANCE PREFERENCES, EXPECTATIONS FORMATION, AND THE DYNAMICS OF RANDOM ASSET PRICES

This paper analyzes the dynamics of an explicit random process of prices and price expectations of finitely many assets in an economy with overlapping generations of heterogeneous consumers. They maximize expected utility with respect to subjective transition probabilities defined by Markov kernels which describe the forecasting behavior of agents. Given such forecasting rules (predictors) and an exogenous process of dividends, the evolution of equilibrium asset prices and expectations is described by a random dynamical system in the sense of Arnold (1998) . The paper investigates the long-run behavior (stationary solutions) by proving the existence and stability of random fixed points for mean-variance preferences under various predictors, including unbiased predictions, and adaptive, as well as OLS forecasting. An explicit characterization of rational expectations solutions is given, providing a full dynamic characterization of asset price processes for the classical CAPM in the case of stationary OLG economies. Numerical simulations are used to compare the performance of the different predictors under an AR(1) dividend process.     

RANDOM WALKS ON REGULAR POLYHEDRA AND OTHER DISTANCE–REGULAR GRAPHS

In this paper we consider Markov chains of the following type: the state space is the set of vertices of a connected, regular graph, and for each vertex transitions are to the adjacent vertices, with equal probabilities. When the mean first–passage matrix F of such a Markov chain is symmetric, the expectation and variance of first–entrance times, recurrence times, number of visits to a vertex and the expectation of the number of different vertices visited, can easily be computed from the entries of F. The method is most effective, when the underlying graph is distance–regular; then F is symmetric and the entries of F can easily be obtained from the graph.