Shrinkage and Orthogonal Decomposition

ABSTRACT. The problem of estimating the mean of a multivariate normal distribution when the parameter space allows an orthogonal decomposition is discussed. Risk functions and lower bounds for a class of shrinkage estimators that includes Stein's estimator are derived, and an improvement on Stein's estimator that takes advantage of the orthogonal decomposition is introduced. Uniform asymptotics related to Pinsker's minimax risk is derived and we give conditions for attaining the lower risk bound. Special cases including regression and analysis of variance are discussed.     

On perfect simulation for some mixtures of distributions

This paper studies the implementation of the coupling from the past (CFTP) method of Propp and Wilson (1996) in the set-up of two and three component mixtures with known components and unknown weights. We show that monotonicity structures can be exhibited in both cases, but that CFTP can still be costly for three component mixtures. We conclude with a simulation experiment exhibiting an almost perfect sampling scheme where we only consider a subset of the exhaustive set of starting values.     

一种用于Ad hoc网络的优化拥塞控制算法

从分析Adhoc网络的信息流竞争特点出发,引入了链路干扰集的概念,构建了一个基于链路干扰集的价格框架。在此框架中,某链路的拥塞信息被表示为此链路干扰集的总价格。并提出了相应的分布式平行算法-价格协同法PCA来求解在小时间段内网络状态不变的拥塞控制问题。同时,分析了PCA对网络状态变化的自适应性能。仿真结果表明,PCA能快速收敛到全局优化解,对网络状态的变化有较好的自适应能力,所获得的网络性能比TCP-Reno要优越得多。     

多次采样与希尔伯特变换

针对文献［１］提出的多次采样方法，证明了多次采样法在一定条件下等效于一种希尔伯特变换器，并用数据证明：当信号的相对带宽小于８０％时，９次采样所获得的正交分量误差可忽略。     

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.     

中国能源消耗减物质化分析

1980-2004年中国能源消耗(生活消耗除外)出现了较大波动,本文运用完全分解模型研究了能源消耗的减物质化过程。结果表明,1980-2004年累计节能量为80790万吨标准煤,同时受国家宏观调控政策的影响,1980-2001年中国能源消耗减物质化处于波动之中,节能缺口幅度大致在1.0之内频繁波动(绝对值),2001年以后中国能源消耗减物质化出现了剧变,节能缺口急剧下降,实际节能率远远低于潜在节能率,能源供应出现了紧张局面。     

On Integer Programming Approaches for Morphological Template Decomposition Problems in Computer Vision

In morphological image processing and analysis, a template or structuringelement is applied to an image. Often savings in computation time and abetter fit to the given computer architecture can be achieved by using thetechnique of template decomposition. Researchers have written a multitude ofpapers on finding such decompositions for special classes of templates.Justifying recent integer programming approaches to the morphologicaltemplate decomposition problem in its general form, this paper proves theNP-completeness of this problem.     

流动人口的自营性收入不平等与工资性收入不平等

利用2005年全国1％人口抽样调查的子样本数据以及2012年流动人口动态监测调查数据,研究了各可观测因素对自营者和受雇者收入分布的解释能力存在何种差异,及其在2005-2012年间所发生的变化.考虑到就业方式选择的内生性问题,文章首先采用转换回归模型对自营者和受雇者的收入方程进行了估计.在此基础上,进一步根据基于回归分析的分解分析方法,利用Shapley分解过程对自营者收入的Gini系数和受雇者收入的Gini系数分别进行了分解分析.结果显示,对于流动人口而言,收入分配过程中的制度性障碍正在减少.     

脉冲响应函数下的我国货币需求变动与决定

基于V A R模型的脉冲响应函数法和预期方差分解法,分析了我国2000年至2013年期间的货币需求与相关经济因素之间的动态影响特征。研究表明狭义货币、广义货币分别与相关的经济变量存在长期的均衡关系。狭义货币、广义货币对GDP的冲击分别呈现抑制效应、对SV的随机冲击主要呈现正效应、对R的随机冲击主要呈现抑制效应、对CPI的冲击呈现正效应和抑制效应交叉出现的现象。狭义货币、广义货币新息冲击对其自身预测均方误差的贡献度最大。新息冲击对狭义货币预测均方误差的贡献依次为：一年期定期存款名义利率（R）、沪深两市A股总市值（SV）、国内生产总值（GDP）、消费者物价指数（CPI）。新息冲击对广义货币预测均方误差的贡献依次为：国内生产总值（GDP ）、消费者物价指数（CPI ）、一年期定期存款名义利率（R ）、沪深两市A股总市值（S V ）。根据实证结论,得出相关经济变量对货币需求的影响,并据此提出有利于完善货币政策的建议。     

Generalized Sobol sensitivity indices for dependent variables: numerical methods

The hierarchically orthogonal functional decomposition of any measurable function η of a random vector X=(X₁,?…?, X_p) consists in decomposing η(X) into a sum of increasing dimension functions depending only on a subvector of X. Even when X₁,?…?, X_p are assumed to be dependent, this decomposition is unique if the components are hierarchically orthogonal. That is, two of the components are orthogonal whenever all the variables involved in one of the summands are a subset of the variables involved in the other. Setting Y=η(X), this decomposition leads to the definition of generalized sensitivity indices able to quantify the uncertainty of Y due to each dependent input in X [Chastaing G, Gamboa F, Prieur C. Generalized Hoeffding–Sobol decomposition for dependent variables – application to sensitivity analysis. Electron J Statist. 2012;6:2420–2448]. In this paper, a numerical method is developed to identify the component functions of the decomposition using the hierarchical orthogonality property. Furthermore, the asymptotic properties of the components estimation is studied, as well as the numerical estimation of the generalized sensitivity indices of a toy model. Lastly, the method is applied to a model arising from a real-world problem.