Local asymptotic normality of intermediate and central order statistics

Consider n independent random variables Z_i,…, Z_n on R with common distribution function F, whose upper tail belongs to a parametric family F(t) = F_θ(t),t ≥ x₀, where θ ∈ ? ? R ^d. A necessary and sufficient condition for the family F_θ, θ ∈ ?, is established such that the k-th largest order statistic Z_n?k+1:n alone constitutes the central sequence yielding local asymptotic normality ( LAN ) of the loglikelihood ratio of the vector (Z_n?i+1:n)¹ _i=kof the k largest order statistics. This is achieved for k = k(n)→_n→∞∞ with k/n→_n→∞ 0.

In the case of vectors of central order statistics ( Z_r:n, Z_r+1:n,…, Z_s:n ), with r/n and s/n both converging to q ∈ ( 0,1 ), it turns out that under fairly general conditions any order statistic Z_m:n with r ≤ m ≤s builds the central sequence in a pertaining LAN expansion.These results lead to asymptotically optimal tests and estimators of the underlying parameter, which depend on single order statistics only     

Testing for Spatial Autocorrelation: The Regressors that Make the Power Disappear

We show that for any sample size, any size of the test, and any weights matrix outside a small class of exceptions, there exists a positive measure set of regression spaces such that the power of the Cliff–Ord test vanishes as the autocorrelation increases in a spatial error model. This result extends to the tests that define the Gaussian power envelope of all invariant tests for residual spatial autocorrelation. In most cases, the regression spaces such that the problem occurs depend on the size of the test, but there also exist regression spaces such that the power vanishes regardless of the size. A characterization of such particularly hostile regression spaces is provided.     

纳入能源环境因素的中国全要素生产率再估算

能源环境约束下的经济增长问题直接推动了全要素生产率的深层次研究.本文基于1995-2010年省际面板数据,利用全局曼奎斯特-卢恩伯格指数法对中国全要素生产率进行再估算,并对其影响因素和收敛性特征进行实证分析.结果发现:纳入能源环境因素的全要素生产率年均增长幅度十分有限,东中西地区呈现递减特征,且增长来源各异;规模因素、管理因素、科技因素、涉外因素等对TFP变化存在正向影响,结构因素存在负向影响;TFP核密度分布呈现双峰特征,与线性回归东部、西部两大地带俱乐部收敛现象互为验证.     

一种基于WebGIS的最优路径导航算法

针对海洋观测设备的回收最优导航问题,提出了一种基于WebGIS的最优路径导航算法．该算法通过改进的最小权值匹配算法求出TSP的最优哈密顿回路,能够为海洋观测设备的回收提供一种有效的导航算法．     

品牌重叠测度理论模型及实证研究

本文在系统梳理现有品牌重叠相关理论的基础上,重新给出了品牌重叠的定义、界定了四种品牌重叠类型。在此基础上以品牌无形特征中的品牌个性构造品牌重叠维度,基于感知品牌定位视角,以中国手机市场为例,对无形感知品牌重叠进行了实证分析。定量分析中本文创新性地引入由最小误判率构造的两两品牌之间的相似矩阵,以此为基础进行MDS分析并用直观的二维图给出了7个手机品牌在品牌个性维度上的相似程度。进一步经过模糊C聚类得到4个手机品牌类别:高功能类、高情感类、中情感低功能类和低情感低功能类。本文最后给出相应的研究结论和未来研究方向。     

基于非线性区间数风险控制的资产负债优化模型

现有研究把存、贷款利率视为常数,无法使资产配置的优化结果适应未来市场利率的变化。本文的资产负债管理优化模型通过资产与负债的区间数的持续期缺口建立了区间型利率风险免疫条件,使资产的最优配置在资产与负债的收益率变化时仍能免疫利率风险。研究表明本文引入的持效期缺口区间的偏向选择参数γ决定预留缺口是赚钱还是亏钱。γ取值0.5时缺口区间两端点的绝对值最小;γ越大于0.5时,正缺口越大,在利率下降时就越赚钱。γ越小于0.5时,负缺口越大,在利率上升时就越赚钱。而区间长度选择参数γ决定损益的大小;揭示了在积极的利率风险管理策略中,选择较小的λ会获得较多的风险收益。另一方面,本文通过相关系数组合半绝对离差建立了非线性区间型组合风险的函数表达式,改变了现有研究线性区间型算法将各笔贷款风险进行简单线性加权、进而夸大了组合信用风险的弊端。     

Competing on Time: An Integrated Framework to Optimize Dynamic Time‐to‐Market and Production Decisions

This study develops a comprehensive framework to optimize new product introduction timing and subsequent production decisions faced by a component supplier. Prior to market entry, the supplier performs process design activities, which improve manufacturing yield and the chances of getting qualified for the customer's product. However, a long delay in market entry allows competitors to enter the market and pass the customer's qualification process before the supplier, reducing the supplier's share of the customer's business. After entering the market and if qualified, the supplier also needs to decide how much to produce for a finite planning horizon by considering several factors such as manufacturing yield and stochastic demand, both of which depend on the earlier time‐to‐market decision. To capture this dependency, we develop a sequential, nested, two‐stage decision framework to optimize the time‐to‐market and production decisions in relation to each other. We show that the supplier's optimal market entry and qualification timing decision need to be revised in real time based on the number of qualified competitors at the time of market‐entry decision. We establish the optimality of a threshold policy. Following this policy, at the beginning of each decision epoch, the supplier should optimally stop preparing for qualification and decide whether to enter the market if her order among qualified competitors exceeds a predetermined threshold. We also prove that the supplier's optimal production policy is a state‐dependent, base‐stock policy, which depends on the time‐to‐market and qualification decisions. The proposed framework also enables a firm to quantify how market conditions (such as price and competitor entry behavior) and operating conditions (such as the rate of learning and inventory/production‐related costs) affect time‐to‐market strategy and post‐entry production decisions.     

生产分割、知识产权保护与出口技术复杂度———基于生产阶段分割的研究视角

国际分工模式已发生根本性改变,全球价值链分工正成为当下世界经济运行的新特征。本文首先基于生产阶段分割法识别与测度了全球主要经济体各自国内和国际生产分割的具体情况,基于此深入考察了生产分割对出口技术复杂度的影响。研究发现,无论是国内生产分割还是国际生产分割,均能够显著提升一国(地区)出口技术复杂度水平。该结果通过一系列内生性和扩展性分析检验,结论依然稳健。另外,在调节效应检验部分,本文还发现知识产权保护对于高技术产品的生产具有不可忽视的调节作用,表明知识产权保护不仅对一国(地区)出口技术复杂度的提升具有直接促进作用,而且还能强化全球生产分割对出口技术复杂度的效果。本文为中国产业向全球价值链中高端攀升提供了理论支持。     

Multiple-output quantile regression through optimal quantization

A new nonparametric quantile regression method based on the concept of optimal quantization was developed recently and was showed to provide estimators that often dominate their classical, kernel-type, competitors. In the present work, we extend this method to multiple-output regression problems. We show how quantization allows approximating population multiple-output regression quantiles based on halfspace depth. We prove that this approximation becomes arbitrarily accurate as the size of the quantization grid goes to infinity. We also derive a weak consistency result for a sample version of the proposed regression quantiles. Through simulations, we compare the performances of our estimators with (local constant and local bilinear) kernel competitors. The results reveal that the proposed quantization-based estimators, which are local constant in nature, outperform their kernel counterparts and even often dominate their local bilinear kernel competitors. The various approaches are also compared on artificial and real data.