Smoothed ranks for two or multi-sample location problems

Smoothed ranks are proposed for two or multi-sample location problems. The regular ranks in Wilcoxon's two sample test are replaced with smoothed ranks, and the shift parameter is estimated. Asymptotic properties of the smoothed rank estimator are shown and a hypothesis test is proposed. Moreover, the smoothed ranks are applied in the Kruskal–Wallis's r-sample test and the power of the test is computed using regular and smoothed ranks. Examples and Monte Carlo simulations show that the smoothed ranks perform similarly to the traditional rank based estimators under contaminated normal or non-normal populations.     

Goodness-of-fit tests for centralized Wishart processes

Abstract

In this paper we present several goodness-of-fit tests for the centralized Wishart process, a popular matrix-variate time series model used to capture the stochastic properties of realized covariance matrices. The new test procedures are based on the extended Bartlett decomposition derived from the properties of the Wishart distribution and allows to obtain sets of independently and standard normally distributed random variables under the null hypothesis. Several tests for normality and independence are then applied to these variables in order to support or to reject the underlying assumption of a centralized Wishart process. In order to investigate the influence of estimated parameters on the suggested testing procedures in the finite-sample case, a simulation study is conducted. Finally, the new test methods are applied to real data consisting of realized covariance matrices computed for the returns on six assets traded on the New York Stock Exchange.     

大规模定制产品柔性分析

提出了一种表达大规模定制产品及其设计参数定制难易程度的指标———柔性指数。基于公理化设计理论,建立了定制方程,导出了柔性指数的计算方法。提出了一种通过计算直接影响设计参数数量和间接影响设计参数数量来比较柔性指数的简便方法。     

基于B-Z反应的企业系统协同演化模型

以典型的具有自组织特点的"B-z"反应模型为基础,应用分析复杂巨系统的协同学思想分析企业系统自组织演化规律.研究表明:利用复杂性科学方法解决经济系统不确定性问题的研究逐步增多,但大部分仍停留在定性描述或构建简单的2维变量模型模拟企业系统演化的阶段.通过引入"B-Z"反应模型可以构建3维变量模型进行企业系统定量描述,使其...     

基于动态模型平均的大豆期货价格预测研究

针对大豆期货价格波动的复杂性及影响因素的多元性,本文将动态模型平均理论引入大豆期货价格分析与预测研究中,通过动态选择解释变量和系数时变程度,在有效控制模型和系数不确定性的同时,最大限度综合利用大豆期货市场内外部信息,以提高大豆期货价格预测准确度。具体的,本文提出一套基于动态模型平均理论的大豆期货价格影响因素与预测分析框架,从期货市场和经济环境等两方面准确地识别出大豆期货价格影响因素的时变特征,进而构建大豆期货价格预测模型,并通过预测误差指标和Diebold-Mariano检验法评估其与基准模型的预测能力。研究结果表明,动态模型平均理论在有效剖析大豆期货价格影响因素的时变特征的同时,能明显提升大豆期货价格预测准确度。     

产业转移、关系投资与产业集群机理研究——来自山东韩资企业的经验数据

外商投资是形成中国产业集群的重要因素,而大批国际产业转移则更能加速其生成。在产业转移的过程中,关系投资的存在进一步强化了产业集群。通过研究发现,产业转移、关系投资、企业自身特性因素以及东道主政府因素等共同决定了外商投资型产业集群生成。基于以上因素,设定若干变量,运用调研微观数据进行参数估计。估计结果表明,关系投资、经营期限、经营状况以及东道主因素对产业集群的生成有显著的正向影响。     

时变参数模型的最优滚动窗宽选择标准及应用

宏观经济领域中存在严重的结构突变性,模型估计量的优劣对估计样本规模是敏感的。本文针对时变参数模型,建立了滚动窗宽选择标准,通过最小化估计量的近似二次损失函数及最大化各子样本估计量间的曼哈顿距离选择窗宽大小,权衡了模型估计量的准确性和时变性两个相悖目标。蒙特卡罗模拟实验表明,本文所提出的方法在各种结构突变情形下均适用,能够应用于线性关系和非线性关系的时变参数模型中,且均具有稳健性。将该方法应用于我国金融网络的结构突变识别过程,显著改善了传统窗宽选择方法的结果。     

A two-phase approach to estimating time-varying parameters in the capital asset pricing model

Following the development of the economy and the diversification of investment, mutual funds are a popular investment tool nowadays. Choosing excellent targets from hundreds of mutual funds has become more and more crucial to investors. The capital asset pricing model (CAPM) has been widely used in the capital cost estimation and performance evaluation of mutual funds. In this study, we propose a new two-phase approach to estimating the time-varying parameters of CAPM. We implemented a simulation study to evaluate the efficiency of the proposed method and compared it with the commonly used state space and rolling regression methods. The results showed that the new method is more efficient in most scenarios. Meanwhile, the proposed approach is very practical and it is unnecessary to judge and adjust the estimating process for different situations. Finally, we applied the proposed method to equity mutual funds in the Taiwan stock market and reported the performances of two funds for demonstration.     

Automatic and asymptotically optimal data sharpening for nonparametric regression

In this article we consider data-sharpening methods for nonparametric regression. In particular modifications are made to existing methods in the following two directions. First, we introduce a new tuning parameter to control the extent to which the data are to be sharpened, so that the amount of sharpening is adaptive and can be tuned to best suit the data at hand. We call this new parameter the sharpening parameter. Second, we develop automatic methods for jointly choosing the value of this sharpening parameter as well as the values of other required smoothing parameters. These automatic parameter selection methods are shown to be asymptotically optimal in a well defined sense. Numerical experiments were also conducted to evaluate their finite-sample performances. To the best of our knowledge, there is no bandwidth selection method developed in the literature for sharpened nonparametric regression.     

Functional principal component analysis via regularized Gaussian basis expansions and its application to unbalanced data

This paper introduces regularized functional principal component analysis for multidimensional functional data sets, utilizing Gaussian basis functions. An essential point in a functional approach via basis expansions is the evaluation of the matrix for the integral of the product of any two bases (cross-product matrix). Advantages of the use of the Gaussian type of basis functions in the functional approach are that its cross-product matrix can be easily calculated, and it creates a much more flexible instrument for transforming each individual's observation into a functional form. The proposed method is applied to the analysis of three-dimensional (3D) protein structural data that can be referred to as unbalanced data. It is shown that our method extracts useful information from unbalanced data through the application. Numerical experiments are conducted to investigate the effectiveness of our method via Gaussian basis functions, compared to the method based on B-splines. On performing regularized functional principal component analysis with B-splines, we also derive the exact form of its cross-product matrix. The numerical results show that our methodology is superior to the method based on B-splines for unbalanced data.