Testing inter-group ranking heterogeneity: do patient characteristics matter for prioritization of quality improvements in healthcare service?

In many research contexts, such as social science, marketing, education, psychology and medicine, it is frequently of interest to compare two or more groups of subjects (e.g. people of different gender, age or nationality), who are asked to rank a set of alternatives according to their personal liking or opinion, for investigating the presence of group effect. The common investigation aim is to detect customers with homogeneous preferences (or priorities) in order to serve each group as properly as possible. Several approaches have been proposed in the literature for testing ranking heterogeneity among groups of subjects. This paper focuses on an approach considering diversity as a generalization of the notion of variation and investigates the performance of a testing procedure for ranking heterogeneity based on the index of segregation power. The performance of the testing procedure has been investigated via a Monte Carlo simulation study under several scenarios, differing for group size, number of ranked alternatives and system of hypothesis. Furthermore, using a real data set, the testing procedure is exploited for investigating whether patient age and gender matter for patient prioritization of quality improvement in healthcare service.     

Testing for Cross-sectional Dependence in Regional Panel Data

Abstract

We examine three tests of cross-sectional dependence and apply them to a Danish regional panel dataset with few time periods and a large cross-section: the CD test due to Pesaran (2004), the Schott test and the Liu–Lin–Shao test. We show that the CD test and the Schott test have good properties in a Monte Carlo study. When controlling for panel-specific and time-specific fixed effects, the Schott test is superior. Our application shows that there is cross-sectional dependence in regional employment growth across Danish regions. We also show that this dependence can be accounted for by time-specific fixed effects. Thus, the tests uncover new properties of the regional data.

RÉSUMÉ Nous examinons trois tests d'autonomie transversale, que nous appliquons à des ensembles de données d'une commission régionale danoise, avec peu de plages de temps et un vaste échantillons: le test d'autonomie transversale mené par Pesaran (2004), le test de Schott et le test Liu–Lin–Shao. Nous démontronsque le test d'autonomie transversale et le test de Schott présentent de bonnes propriétés dans uneétude Monte Carlo. Lorsquel'on se penchesur les effets propres au panel et les effets fixes en fonction du temps, le test de Schott dépasse les autres. Notre application illustre le dépendance transversale dans l'expansion de l'emploi à l’échelon régional dans les régions danoises, et nous démontronsque cette dépendance peut s'expliquer par des effets fixes en fonction du temps. De cette façon, le test révèle des propriétés nouvelles des données régionales.

RESUMEN Examinamos tres pruebas de dependencia transversal y las aplicamos a un conjunto de datos de panel regional en Dinamarca con pocos períodos de tiempo y una gran sección transversal: la prueba CD de Pesaran (2004), la prueba Schott y la prueba Liu–Lin–Shao. Demostramos que la prueba CD y la prueba Schott poseen buenas propiedades en un estudio de Monte Carlo. Al controlar los efectos fijos específicos de panel y específicos de tiempo, la prueba Schott es superior. Nuestra aplicación demuestra que existe una dependencia transversal en el crecimiento del empleo regional a través de las regiones danesas. Asimismo, demostramos que esta dependencia puede ser explicada mediante los efectos fijos específicos de tiempo. Por lo tanto, las pruebas dejan al descubierto nuevas propiedades de los datos regionales.

     

A Comparative Analysis of Different IV and GMM Estimators of Dynamic Panel Data Models

It is well known that the usual procedures for estimating panel data models are inconsistent in the dynamic setting. A large number of consistent estimators however, have been proposed in the literature. This paper provides a survey of the majority of mainstream estimators, which tend to consist of IV and GMM ones. It also considers a newly proposed extension to the promising Wansbeek–Bekker estimator (Harris & Mátyás, 2000). To provide guidance to the applied researcher working on micro-datasets, the small sample performance of these estimators is evaluated using a set of Monte Carlo experiments.     

基于蒙特卡洛的输电线路清单招投标风险研究

输电线路工程受外界因素影响大,造成招标工程量与竣工工程量存在差异,从而引起投标单位采用不平衡报价导致结算超概算情况的出现,增加了建设单位的管理风险。在深入分析输电线路风险变量的基础上,构建出基于蒙特卡洛模拟的风险评估模型,并通过实例,对投标报价的风险概率进行了直观描述,提出了建设性意见。     

Methods for the Analysis of Explanatory Linear Regression Models with Missing Data Not at Random

Since the work of Little and Rubin (1987) not substantial advances in the analysisof explanatory regression models for incomplete data with missing not at randomhave been achieved, mainly due to the difficulty of verifying the randomness ofthe unknown data. In practice, the analysis of nonrandom missing data is donewith techniques designed for datasets with random or completely random missingdata, as complete case analysis, mean imputation, regression imputation, maximumlikelihood or multiple imputation. However, the data conditions required to minimizethe bias derived from an incorrect analysis have not been fully determined. In thepresent work, several Monte Carlo simulations have been carried out to establishthe best strategy of analysis for random missing data applicable in datasets withnonrandom missing data. The factors involved in simulations are sample size,percentage of missing data, predictive power of the imputation model and existenceof interaction between predictors. The results show that the smallest bias is obtainedwith maximum likelihood and multiple imputation techniques, although with lowpercentages of missing data, absence of interaction and high predictive power ofthe imputation model (frequent data structures in research on child and adolescentpsychopathology) acceptable results are obtained with the simplest regression imputation.     

金融资产厚尾分布及常用的风险度量

本文首先运用正态分布、带有位置-尺度参数的t分布、logistic分布、极值分布、-stable分布和核密度估计对上证综指收益率分布进行拟合,结果表明核密度估计优于其他分布。其次,在进行尾部风险拟合和度量风险方面,通过设定相关指标,在显著性水平为1%时,-stable分布更适合衡量风险程度,在此基础上提出了调和-stable分布,并得到一个同构表示解。最后,本文给出了蒙特卡洛-stable分布模拟和经验值下的MDD、DaR和CDaR,并得到了模型值和经验值之间的乘离率。     

ON THE PARAMETERIZATION OF TECHNIQUES FOR REPRESENTING REGIONAL ECONOMIC STRUCTURES

This paper is concerned with two parameterized methods of regionalising input–output coefficients: the Flegg et al. Location Quotient (FLQ) and its augmented version (AFLQ). For applying the two techniques, a parameter δ has to be estimated. In this regard, the paper faces two matters that are still open in the literature: the existence of a range of δ that can be used in different regions and the estimation of the most appropriate value of δ. For this aim, a Monte Carlo simulation has been carried out in order to generate ‘true’ multiregional I-O tables randomly. From the simulation, analyses based on probability distributions and regression were also carried out. Finally, these simulation results have been compared with those of an empirical case. Results confirm that there is actually a range of values of δ within which the best δ is more likely to fall. For the FLQ, this range is centred on 0.3 with an associated probability of 33% (if the width of the range is set at 0.1), whereas, for the AFLQ, the relevant range is between 0.3 and 0.4 with a probability by 38%. Finally, this paper provided a way to estimate the best δ for a given region, without knowing the relevant and detailed economic structure at sectoral level.     

Recovering the Metric Structure in Ordinal Data: Linear Versus Nonlinear Principal Components Analysis

Two techniques for data reduction as part of the SPSS package are compared in a Monte Carlo study: principal components analysis (PCA) and nonlinear principal components analysis (NPCA). The relative performance of these techniques in recovering the component scores underlying subjects' scores on observed ordinal variables is studied for two-dimensional spaces. The relative performance is examined as a function of (a) the sample size, (b) the number of categories in the variables, (c) the amount of measurement error, (d) the type of nonlinearity in the data, and (e) the degree of heterogeneity of the marginal distributions of the variables. As expected, when the sample size increases the performance of NPCA improves when compared to PCA. For the range of values considered, there is no effect of the number of categories on the relative performance of PCA and NPCA. For the other factors the effects are more complicated: adding error does not affect PCA as strongly as NPCA, as expected, but not for heterogeneously distributed variables for a particular form of nonlinearity, in which case NPCA becomes more appropriate. PCA appears to outperform NPCA for linear data, but also for a substantial number of nonlinear data sets.     

Finite sample properties of the QMLE for the Log-ACD model: Application to Australian stocks

This paper concerns the properties of the Quasi Maximum Likelihood Estimator (QMLE) of the Logarithmic Autoregressive Conditional Duration (Log-ACD) model. Proofs of consistency and asymptotic normality of QMLE for the Log-ACD model with log-normal density are presented. This is an important issue as the Log-ACD is used widely for testing various market microstructure models and effects. Knowledge of the distribution of the QMLE is crucial for purposes of valid inference and diagnostic checking. The theoretical results developed in the paper are evaluated using Monte Carlo experiments. The experimental results also provide insights into the finite sample properties of the Log-ACD model under different distributional assumptions. Finally, this paper presents two extensions to the Log-ACD model to accommodate asymmetric effects. The usefulness of these novel models will be evaluated empirically using data from Australian stocks.     

Autocorrelation and Bias in Short Time Series: An Alternative Estimator

The conventional first-order autocorrelationcoefficient r₁ generates an empiricalbias when it is applied to short time series.The properties of this estimator have beenexamined with a Monte Carlo simulation studyusing the MATLAB program (version5.2). This study also analyzes the functionof the empirical bias with the polynomicregression and derives a polynomic fittingmodel for different sample sizes. In thisway, a new estimator that has been correctedby the absolute value of the fitting model(r_1') is proposed. Having analyzed thestatistical properties of the estimator r_1',it is shown that the empirical bias generatedby r_1' is less in relationship to r₁ andr₁₊. The results of the study make itpossible to verify that the mean squared errorassociated to the estimator r₁ isless than that of r₁. Thus, the coefficient r_1'is recommended to estimate the lag-oneautocorrelation coefficient in samples under 50observations.     

供应链延迟战略价值定量分析——蒙特卡罗下的奇异期权估价

对延迟战略建立两阶段决策模型,分半成品有无残值两种情况,从实物期权的视角运用金融学中期权定价理论对延迟战略的期权价值进行分析。将生产商传统生产方式下的收益类比为购买标的证券的收益,采用延迟战略的收益类比为标的于该证券的期权收益,并假设产品价格随机游走。通过分析发现延迟战略的收益相当于奇异期权的回报,并且半成品没有残值是存在残值的特殊情况。进一步运用蒙特卡罗模拟方法定量地对延迟战略的期权价值进行参数分析和成本一收益分析。文章将动态的风险管理和对灵活性价值的度量引入决策过程,研究结论能给延迟战略投资决策提供借鉴。     

随机数在随机事件与概率教学中的应用探索

本文对在随机事件与概率中的常见问题提出了一种新的借助于计算机实现的解决思路,即通过机器自动产生随机数后构建相应的数学模型,再通过编程计算得到最后结果。该方法既为概率论的教学带来了创新性思维,也为当前的教师提出了新的挑战。     

天津市气温的随机模型

气温是大自然最常见的现象,对其预测有多种模型。文章依据采用广为使用的Ornstein-Uhlenbeck过程来建立气温变化的随机模型,并用天津2000年至2009年日气温对模型参数进行估计,并详细求解了模型。     

不确定条件下对称企业R&D项目的期权博弈分析

为了对市场不确定性条件下的企业项目价值进行评价,文章通过构建期权博弈模型对对称企业的研究与开发(R&D)项目投资行为进行了研究,并采用了最小二乘法的蒙特卡罗模拟(LSM)方法对模型进行了仿真求解,得到了两企业R&D项目的价值。研究结果显示,LSM方法与期权博弈相结合做法具有可行性,对企业进行项目价值的评估具有参考价值和指导意义。     

Fast delta computations in the swap-rate market model

We develop an efficient algorithm to implement the adjoint method that computes sensitivities of an interest rate derivative to different underlying rates in the co-terminal swap-rate market model. The order of computation per step of the new method is shown to be proportional to the number of rates times the number of factors, which is the same as the order in the LIBOR market model.     

蒙特卡洛法在投资项目风险分析中的应用

本文以经济净现值为评价指标来度量项目的投资风险,确定各影响因素的概率分布,建立了基于三角分布的风险评价的随机模型,采用蒙特卡罗方法进行模拟,利用MATLAB编程实现,得到投资项目的净现值频数分布的直方图和累计频率曲线图,并对模拟结果进行统计和分析,可得到净现值的平均预测值以及风险率,为评价投资项目的风险提供了理论依据。