Inverse probability weighted estimation of local average treatment effects: A higher order MSE expansion

We consider a new inverse probability weighted estimator for the local average treatment effect parameter where the instrument propensity score is estimated by local polynomial regression. We derive its asymptotics and provide a higher order expansion of its asymptotic MSE.     

基于EM算法的混合自回归滑动平均模型的参数估计

研究了一类用于时间序列建模的混合自回归滑动平均模型.该模型是由m个ARMA分量经过混合得到的,给出了混合自回归滑动平均模型参数估计的期望极大化(EM)算法,从而得到了混合系数和分量模型的参数,通过仿真说明了其有效性.     

The estimation of a nonlinear moving average model

We consider discrete-parameter stochastic processes that are the output of a nonlinear filter driven by white noise. For a simple model, we derive estimates of the unknown coefficients in the transfer function and the noise variance, and investigate their asymptotic properties. We prove some lemmas that can also be used to obtain rates of convergence in the weak and strong laws of large numbers, and central limit theorems, for estimates of more general nonlinear models.     

Semiparametric estimation of average treatment effect through a random coefficient dummy endogenous variable model

This paper provides an estimation procedure for average treatment effect through a random coefficient dummy endogenous variable model. A leading example of the model is estimating the effect of a training program on earnings. The model is composed of two equations: an outcome equation and a decision equation. Given the linear restriction in outcome and decision equations, Chen (1999) provided a distribution-free estimation procedure under conditional symmetric error distributions. In this paper we extend Chen’s estimator by relaxing the linear index into a nonparametric function, which greatly reduces the risk of model misspecification. A two-step approach is proposed: the first step uses a nonparametric regression estimator for the decision variable, and the second step uses an instrumental variables approach to estimate average treatment effect in the outcome equation. The proposed estimator is shown to be consistent and asymptotically normally distributed. Furthermore, we investigate the finite performance of our estimator by a Monte Carlo study and also use our estimator to study the return of college education in different periods of China. The estimates seem more reasonable than those of other commonly used estimators.     

Aggregation of ordinal information

Our interest is with the fusion of information which has an ordinal structure. Information fusion in this environment requires the availability of ordinal aggregation operations. Basic ordinal operations are first introduced. Next we investigate conjunctive and disjunction aggregations of ordinal information. The idea of a pseudo-log in the ordinal environment is presented. We discuss the introduction of a zero like point on an ordinal scale along with the related ideas of bipolarity (positive and negative values) and uni-norm aggregation operators. We introduce mean like aggregation operators as well weighted averages on a ordinal scale. The problem of selecting between ordinal models is considered.     

非线性Black-Scholes模型下几何平均亚式期权定价

在非线性Black-Scholes模型下,本文研究了几何平均亚式期权定价问题.首先利用单参数摄动方法,将亚式期权适合的偏微分方程分解成一系列常系数抛物方程.其次通过计算这些常系数抛物型方程的解,给出了几何平均亚式期权的近似定价公式.最后利用Green函数分析了近似结论的误差估计.     

On rates of convergence of information theoretic criterion in rank determination of one-way random effects models

In this paper, upper bounds on the probabilities of wrong determination of the rank of covariance matrix of random effects in one-way random effects models are given, based on the information theoretic criterion. Under weak conditions, the bounds are shown of exponential-type.The work was partially supported by Natural Sciences and Engineering Research Council of Canada and Faculty Research Grant, Faculty of Arts, York University.     

基于等维递补的多变量灰色组合预测模型

利用等维递补建立单个多变量灰色预测模型,进而建立多变量灰色组合预测模型.通过实例说明多变量灰色组合模型的应用和效果.     

对加权蛛网模型稳定性的探讨

在传统的蛛网模型中引入权值,用以反映生产者对未来市场的预测和决策,并利用动力系统知识研究了需求函数y=f(x)和供应函数x=g(y)交点(即平衡点)的稳定性条件.当用近两周期价格加权平均预测下一周期商品的价格,从而确定生产数量时,即x_(k+1)=g(λy_k+y_(k-1))/(1+λ)),对任意的λ∈(0,+∞),证明了当λ=2时,平衡点的稳定区域最大(αβ3),其中-α,β分别为需求函数和供应函数在平衡点处的斜率;当用近三个周期价格加权平均预测下一周期商品的价格,从而确定生产数量时,即xk+1=g(λy_k+μy_(k-1)+y_(k-1))/(1+λ+μ),证明了当λ=5,μ=4时,平衡点的稳定区域为αβ5.与传统的蛛网模型相比,平衡点的稳定区域扩大了,从而更有利于经济趋于稳定.     

Parameter estimation of varying coefficients structural EV model with time series

In this paper, the parameters of a p-dimensional linear structural EV (error-in-variable) model are estimated when the coefficients vary with a real variable and the model error is time series. The adjust weighted least squares (AWLS) method is used to estimate the parameters. It is shown that the estimators are weakly consistent and asymptotically normal, and the optimal convergence rate is also obtained. Simulations study are undertaken to illustrate our AWLSEs have good performance.     

Interval-valued probability density estimation based on quasi-continuous histograms: Proof of the conjecture

The sensitivity of histogram computation to the choice of a reference interval and number of bins can be attenuated by replacing the crisp partition on which the histogram is built by a fuzzy partition. This involves replacing the crisp counting process by a distributed (weighted) voting process. The counterpart to this low sensitivity is some confusion in the count values: a value of 10 in the accumulator associated with a bin can mean 10 observations in the bin or 40 observations near the bin. This confusion can bias the statistical decision process based on such a histogram. In a recent paper, we proposed a method that links the probability measure associated with any subset of the reference interval with the accumulator values of a fuzzy partition-based histogram. The method consists of transferring counts associated with each bin proportionally to its interaction with the considered subset. Two methods have been proposed which are called precise and imprecise pignistic transfer. Imprecise pignistic transfer accounts for the interactivity of two consecutive cells in order to propagate, in the estimated probability measure, counting confusion due to fuzzy granulation. Imprecise pignistic transfer has been conjectured to include precise pignistic transfer. The present article proposes a proof of this conjecture.     

The effects of mental model formation on group decision making: An agent‐based simulation

We investigated dynamics of group decision making on complex problems when agents can form mental models of others from discussion history. Results indicated that as the agents' memory capacity increases, the group reaches superficial consensus more easily. Surprisingly, however, the shared mental model of the problem develops only within a limited area of the problem space, because incorporating knowledge from others into one's own knowledge quickly creates local agreement on where relevant solutions are, leaving other potentially useful solutions beyond the scope of discussion. The mechanisms stifling group‐level exploration and their implications for decision making research are discussed. © 2010 Wiley Periodicals, Inc. Complexity 16: 49–57, 2011     

A model for diffusion of mulucomponent information

The epidemic model of diffusion of news (or disease) is generalized to describe the diffusion of a multi‐component information. The multivaluedness of information in our model arises due to the large number (k) of constituent components or items of the information in question. When the different components of information are assumed to bear no hierarchy, the master equation of the model contains an intractably large number of variables (2 ^k ). The dynamics of the model, however, displays some simplifying features, one of which is the conservation of homogeneity of distribution of population over the different information vectors (in the sense defined in the text). The homogenized version of the model is found to be numerically tractable. The growth curves for large k continue to display sigmoid shapes, but with large ‘saturation times’. The dependence of ‘saturation time’ (i.e. the time required for spread of all the information in almost the entire population) on various parameters of the model, for uniform initial distributions, is numerically investigated. The ‘saturation time’ is found to vary inversely with the intensity of interaction (ß) and the size of population (N), as expected. An important numerical feature that emerges is that the ‘saturation time’ seems to be in linear proportion to the number of information items (k).     

基于韧性度的低轨卫星通信网络抗毁性度量及优化

低轨卫星通信网络的抗毁性是描述网络安全可靠的有效工具,在网络体系结构设计和路由策略等领域得到了广泛的应用。根据低轨卫星通信网络中卫星在轨道平面内移动,需要不断进行切换的特点,从建立抗毁性测度模型以及网络抗毁性优化两个角度来评估和提高网络抗毁性,提出一种基于韧性度的低轨卫星通信网络抗毁性度量方法。通过对移动模型以及切换模型的结构分析,对每种结构以一定概率出现的低轨卫星通信网络,应用韧性度函数,求得网络在某个时刻及某一段时间段内的抗毁性,并针对切换模型的不足之处进行优化,用赋权韧性度来体现优化的效果,得到了优化后的网络抗毁性。以铱星系统为应用实例进行仿真,结果表明:任意时刻网络的抗毁性跟拓扑结构的韧性度值有关,并且是一种线性关系,即随着韧性度的增加,其抗毁性也增加。通过对铱星通信系统切换模型的优化,网络的抗毁性与平均抗毁性都得到了提升,说明本文所构建模型的有效性和实用性。     

Multistrain edge‐based compartmental model on networks

Multistrain diseases, which are infected through individual contacts, pose severe public health threat nowadays. In this paper, we build competitive and mutative two‐strain edge‐based compartmental models using probability generation function (PGF) and pair approximation (PA). Both of them are ordinary differential equations. Their basic reproduction numbers and final size formulas are explicitly derived. We show that the formula gives a unique positive final epidemic size when the reproduction number is larger than unity. We further consider competitive and mutative multistrain diseases spreading models and compute their basic reproduction numbers. We perform numerical simulations that show some dynamical properties of the competitive and mutative two‐strain models.     

Model selection method based on maximal information coefficient of residuals

The traditional model selection criterions try to make a balance between fitted error and model complexity. Assumptions on the distribution of the response or the noise, which may be misspecified, should be made before using the traditional ones. In this article, we give a new model selection criterion, based on the assumption that noise term in the model is independent with explanatory variables, of minimizing the association strength between regression residuals and the response, with fewer assumptions. Maximal Information Coefficient (MIC), a recently proposed dependence measure, captures a wide range of associations, and gives almost the same score to different type of relationships with equal noise, so MIC is used to measure the association strength. Furthermore, partial maximal information coefficient (PMIC) is introduced to capture the association between two variables removing a third controlling random variable. In addition, the definition of general partial relationship is given.     

Extended rough set model based on known same probability dominant valued tolerance relation

Classical rough set theory is based on the conventional indiscernibility relation. It is not suitable for analyzing incomplete information. Some successful extended rough set models based on different non-equivalence relations have been proposed. The data-driven valued tolerance relation is such a non-equivalence relation. However, the calculation method of tolerance degree has some limitations. In this paper, known same probability dominant valued tolerance relation is proposed to solve this problem. On this basis, an extended rough set model based on known same probability dominant valued tolerance relation is presented. Some properties of the new model are analyzed. In order to compare the classification performance of different generalized indiscernibility relations, based on the category utility function in cluster analysis, an incomplete category utility function is proposed, which can measure the classification performance of different generalized indiscernibility relations effectively. Experimental results show that the known same probability dominant valued tolerance relation can get better classification results than other generalized indiscernibility relations.     

基于熵权的投资评价模型在风险投资中的应用

本着“实用性和现实操作性”原则,本文根据风险投资评价的实际操作,在引入粗糙集信息熵理论,导出基于多指标评价的熵权投资模型的基础上,通过问卷调查的实证研究方法,确定评价指标和权重,并例举实际(经适当简化)案例演算具体运算过程,以验证在实际风险投资中的可操作性。从而试图克服目前相关领域研究文献基本停留在方法研究阶段、所给的证例过于简单、没有实际运用价值的缺陷,也尝试探索粗糙集理论在风险投资管理中的应用。     

Random effects logistic regression model for default prediction of technology credit guarantee fund

Korean government has been funding the small and medium enterprises (SME) with superior technology based on scorecard. However high default rate of funded SMEs has been reported. In order to effectively manage such governmental fund, it is important to develop accurate scoring model for SMEs. In this paper, we provide a random effects logistic regression model to predict the default of funded SMEs based on both financial and non-financial factors. Advantage of such a random effects model lies in the ability of accommodating not only the individual characteristics of each SME but also the uncertainty that cannot be explained by such individual factors. It is expected that our study can contribute to effective management of government funds by proposing the prediction models for defaults of funded SMEs.