GRAPHICAL REPRESENTATION OF THE EFFECTS OF MULTICOLLINEARITY

This paper considers the effects of multicollinearity on the multiple coefficient of determination and on the standard errors of the estimated regression coefficients. Limiting results are well known for these problems, and some particular cases have been investigated. However, the limiting cases do not explain the nonmonotonic relationships that have been observed in practice. This paper explores these relationships and attempts to provide a unifying graphical explanation that includes the previous results as special cases. Implications for stepwise regression and for the interpretation of any multiple regression involving multicollinear independent variables are discussed.     

Longitudinal data analysis: Applications of random coefficient modeling to leadership research

Understanding how leaders develop, adapt, and perform over time is central to many theories of leadership. However, for a variety of conceptual and methodological reasons, such longitudinal research remains uncommon in the leadership domain. The purpose of this paper is to introduce the leadership scholar to conceptual issues involved in longitudinal design and analysis, and then demonstrate the application of random coefficient modeling (RCM) as a framework capable of modeling longitudinal leadership data. The RCM framework is an extension of the traditional regression model, so many readers will already have the fundamental knowledge required to use RCM. However, RCM has the additional capability of analyzing the kinds of data commonly found in longitudinal studies, including correlated observations, missing data, and heterogeneity over time. Further, the RCM allows for testing predictors of change over time. Thus, we introduce conceptual issues related to longitudinal research, discuss RCM within the context of regression, and conclude with an application of the RCM approach. We use a common substantive example throughout the paper to facilitate our discussion of the RCM.     

Local Partitioned Regression

In this paper, we introduce a kernel‐based estimation principle for nonparametric models named local partitioned regression (LPR). This principle is a nonparametric generalization of the familiar partition regression in linear models. It has several key advantages: First, it generates estimators for a very large class of semi‐ and nonparametric models. A number of examples that are particularly relevant for economic applications will be discussed in this paper. This class contains the additive, partially linear, and varying coefficient models as well as several other models that have not been discussed in the literature. Second, LPR‐based estimators achieve optimality criteria: They have optimal speed of convergence and are oracle‐efficient. Moreover, they are simple in structure, widely applicable, and computationally inexpensive. A Monte Carlo simulation highlights these advantages.     

Estimation When a Parameter is on a Boundary

This paper establishes the asymptotic distribution of an extremum estimator when the true parameter lies on the boundary of the parameter space. The boundary may be linear, curved, and/or kinked. Typically the asymptotic distribution is a function of a multivariate normal distribution in models without stochastic trends and a function of a multivariate Brownian motion in models with stochastic trends. The results apply to a wide variety of estimators and models. Examples treated in the paper are: (i) quasi-ML estimation of a random coefficients regression model with some coefficient variances equal to zero and (ii) LS estimation of an augmented Dickey-Fuller regression with unit root and time trend parameters on the boundary of the parameter space.     

Learning curve analysis in total productive maintenance

The continuous improvement concepts such as total quality management, just-in-time and total productive maintenance have been widely recognized as a strategic weapon and successfully implemented in many organizations. In this paper, we focus on the application of total productive maintenance (TPM). A random effect non-linear regression model called the Time Constant Model was used to formulate a prediction model for the learning rate in terms of company size, sales, ISO 9000 certification and TPM award year. A two-stage analysis was employed to estimate the parameters. Using the approach of this study, one can determine the appropriate time for checking the performance of implementing total productive maintenance. By comparing the expected overall equipment effectiveness (OEE), one can improve the maintenance policy and monitor the progress of OEE.     

Testing When a Parameter is on the Boundary of the Maintained Hypothesis

This paper considers testing problems where several of the standard regularity conditions fail to hold. We consider the case where (i) parameter vectors in the null hypothesis may lie on the boundary of the maintained hypothesis and (ii) there may be a nuisance parameter that appears under the alternative hypothesis, but not under the null. The paper establishes the asymptotic null and local alternative distributions of quasi‐likelihood ratio, rescaled quasi‐likelihood ratio, Wald, and score tests in this case. The results apply to tests based on a wide variety of extremum estimators and apply to a wide variety of models. Examples treated in the paper are: (i) tests of the null hypothesis of no conditional heteroskedasticity in a GARCH(1, 1) regression model and (ii) tests of the null hypothesis that some random coefficients have variances equal to zero in a random coefficients regression model with (possibly) correlated random coefficients.     

Cost of Equity in Homeland Security Resource Allocation in the Face of a Strategic Attacker

Hundreds of billions of dollars have been spent in homeland security since September 11, 2001. Many mathematical models have been developed to study strategic interactions between governments (defenders) and terrorists (attackers). However, few studies have considered the tradeoff between equity and efficiency in homeland security resource allocation. In this article, we fill this gap by developing a novel model in which a government allocates defensive resources among multiple potential targets, while reserving a portion of defensive resources (represented by the equity coefficient) for equal distribution (according to geographical areas, population, density, etc.). Such a way to model equity is one of many alternatives, but was directly inspired by homeland security resource allocation practice. The government is faced with a strategic terrorist (adaptive adversary) whose attack probabilities are endogenously determined in the model. We study the effect of the equity coefficient on the optimal defensive resource allocations and the corresponding expected loss. We find that the cost of equity (in terms of increased expected loss) increases convexly in the equity coefficient. Furthermore, such cost is lower when: (a) government uses per‐valuation equity; (b) the cost‐effectiveness coefficient of defense increases; and (c) the total defense budget increases. Our model, results, and insights could be used to assist policy making.     

Classification of Listeria monocytogenes Persistence in Retail Delicatessen Environments Using Expert Elicitation and Machine Learning

Increasing evidence suggests that persistence of Listeria monocytogenes in food processing plants has been the underlying cause of a number of human listeriosis outbreaks. This study extracts criteria used by food safety experts in determining bacterial persistence in the environment, using retail delicatessen operations as a model. Using the Delphi method, we conducted an expert elicitation with 10 food safety experts from academia, industry, and government to classify L. monocytogenes persistence based on environmental sampling results collected over six months for 30 retail delicatessen stores. The results were modeled using variations of random forest, support vector machine, logistic regression, and linear regression; variable importance values of random forest and support vector machine models were consolidated to rank important variables in the experts’ classifications. The duration of subtype isolation ranked most important across all expert categories. Sampling site category also ranked high in importance and validation errors doubled when this covariate was removed. Support vector machine and random forest models successfully classified the data with average validation errors of 3.1% and 2.2% (n = 144), respectively. Our findings indicate that (i) the frequency of isolations over time and sampling site information are critical factors for experts determining subtype persistence, (ii) food safety experts from different sectors may not use the same criteria in determining persistence, and (iii) machine learning models have potential for future use in environmental surveillance and risk management programs. Future work is necessary to validate the accuracy of expert and machine classification against biological measurement of L. monocytogenes persistence.     

A random walk model of skin permeation.

A new mathematical model for permeability of chemicals in aqueous vehicle through skin is presented. The rationale for this model is to represent diffusion by its fundamental molecular mechanism, i.e., random thermal motion. Diffusion is modeled as a two-dimensional random walk through the biphasic (lipid and corneocyte) stratum corneum (SC). This approach permits calculations of diffusion phenomena in a morphologically realistic SC structure. Two concepts are key in the application of the model to the prediction of steady-state skin permeability coefficients: "effective diffusivity" and "effective path length," meaning the diffusivity and thickness of a homogeneous membrane having identical permeation properties as the stratum corneum. Algebraic expressions for these two variables are developed as functions of the molecular weight and octanol-water partition coefficient of the diffusing substance. Combining these with expressions for membrane-vehicle partition coefficient and permeability of the aqueous epidermis enables the calculation of steady-state skin permeability coefficients. The resulting four-parameter algebraic model was regressed against the "Flynn data base" with excellent results (R2 = 0.84: SE = 0.0076; F = 154; N = 94). The model provides insight into the contributions of stratum corneum diffusivity and effective path lengths to overall skin permeability and may prove useful in the prediction of non-steady-state diffusion phenomena.     

Jump Regressions

We develop econometric tools for studying jump dependence of two processes from high‐frequency observations on a fixed time interval. In this context, only segments of data around a few outlying observations are informative for the inference. We derive an asymptotically valid test for stability of a linear jump relation over regions of the jump size domain. The test has power against general forms of nonlinearity in the jump dependence as well as temporal instabilities. We further propose an efficient estimator for the linear jump regression model that is formed by optimally weighting the detected jumps with weights based on the diffusive volatility around the jump times. We derive the asymptotic limit of the estimator, a semiparametric lower efficiency bound for the linear jump regression, and show that our estimator attains the latter. The analysis covers both deterministic and random jump arrivals. In an empirical application, we use the developed inference techniques to test the temporal stability of market jump betas.     

Educational objectives and measurement of outcomes

Correlation and regression analysis are often used to infer causal relationships in dynamic systems, even though computed on cross-sectional static data. In education these analytic techniques have been used to support assertions that school-controlled variables make little contribution to student learning. Critics of these assertions point to the low quality of the data, but it may be that the techniques themselves are inappropriate for the development of inferences of causality. This study simulated four possible models of dynamic relationships between family and school inputs and achievement outcomes. The models were run for five periods. Data generated were submitted to correlation and regression analysis. Both unique variance and regression coefficient indicators failed to describe reliably causal relationships built into the models. Conclusion: complex systems resist simplistic analyses.     

A mixed inventory model with variable lead time and random supplier capacity

In this paper, we have studied analytically the implication of a controllable lead-time and a random supplier capacity on the continuous review inventory policy, in which the order quantity, reorder point and lead-time are decision variables. Two models are considered: the normal lead-time demand and lead-time demand is distributed free. For both cases, after formulating the general model, some properties of the optimal ordering policy have been developed. Particularly, we have shown that the expected annual total cost is a unimodal function and quasi-convex in the order quantity. When the variable capacity distribution is exponential, we develop effective procedures for finding the optimal solutions. Furthermore, the effects of parameters are also performed.     

Identification in Nonseparable Models

Weak nonparametric restrictions are developed, sufficient to identify the values of derivatives of structural functions in which latent random variables are nonseparable. These derivatives can exhibit stochastic variation. In a microeconometric context this allows the impact of a policy intervention, as measured by the value of a structural derivative, to vary across people who are identical as measured by covariates. When the restrictions are satisfied quantiles of the distribution of a policy impact across people can be identified. The identification restrictions are local in the sense that they are specific to the values of the covariates and the specific quantiles of latent variables at which identification is sought. The conditions do not include the commonly required independence of latent variables and covariates. They include local versions of the classical rank and order conditions and local quantile insensitivity conditions. Values of structural derivatives are identified by functionals of quantile regression functions and can be estimated using the same functionals applied to estimated quantile regression functions.     

产业生命周期不同阶段的最优集体创新网络结构

本文采用仿真方法从创新效率的角度对产业生命周期不同阶段下的最优集体创新网络结构进行了研究。研究发现,在产业生命周期的导入期,以较高的平均聚集系数为特征的规则网络具有最高的集体创新效率;在产业生命周期的成长期,以较高的小世界系数为特征的小世界网络具有最高的集体创新效率;当产业生命周期进入成熟期以后,以较短的最短路径长度为特征的随机网络具有最高的集体创新效率。本文通过对上述结果的进一步分析得出,上述结果是由以下三个层次的原因造成的。第一,产业生命周期的不同阶段具有不同的产业知识特征和技术机会。第二,产业知识特征会影响产业内部的知识流动和企业实现知识重组的能力,而技术机会的多少会影响企业搜寻并发现创新机会的能力。第三,较高的网络平均聚集系数有利于促进知识流动,而较短的平均最短路径长度有利于企业搜寻并发现创新机会。最后,本文提出了以上结论对创新政策制定者的一些重要启示。     

供应链收入共享契约协调的随机期望值模型

本文研究了供应链契约协调中的收入共享契约.建立了具有缺货成本的供应链收入共享契约协调的随机期望值模型,推导了实现分散供应链协调的收入共享契约参数之间的关系,分析了收入共享机制在改进供应链整体运作绩效方面的作用.设计了求解随机期望值模型的混合智能算法,进行了收入共享契约协调模型仿真实验,探讨了收入共享契约参数变化对供应链成员绩效的影响.结果表明,本文建立的收入共享契约协调的随机期望值模型具有实际意义,并在随机需求条件下协调了供应链运作.     

Leader behaviors and career derailment potential: A multi-analytic method examination of rating source and self–other agreement

Based on extensive research that views leadership as a multi-faceted phenomenon, we examined how the relationships between task-oriented and relationship-oriented leader behaviors and career derailment potential vary by observer perspective. We present findings using three different analytical techniques: random coefficient modeling (RCM), relative weight analysis (RWA), and polynomial regression (PR). RCM findings suggest that self-, direct report, peer, and supervisor ratings of leader behaviors differ and are associated with career derailment potential. RWA results indicate that self-ratings matter the least, whereas peer ratings of leader behaviors typically matter the most in predicting career derailment potential. PR analyses indicate that career derailment potential is lowest when self-ratings are lower than other ratings of leader behaviors and/or when self–other ratings converge on higher, rather than lower, ratings of leader behaviors. Implications for leadership and self–other agreement research and professional practice are discussed.     

The contribution of outward direct investment to productivity changes within China, 1991–2007

There has been a marked growth in recent years in outward direct investment (ODI) by developing countries, and in particular, by China. Previous studies have examined the impact on developing countries productivity of foreign direct investment (FDI) from developed countries. This paper looks at the effects of China's outward direct investment on growth in its own productivity, and at two specific reasons for this growth: technology sourcing and improvements in efficiency. These are examined using data from China's ODI in eight developed countries during the period 1991 to 2007. It appears that Chinese outward direct investment has had beneficial spill-over effects in improving total factor productivity growth over the period of the study, and that gains in efficiency have been the chief reason for this. Our vector auto regression (VAR) decomposition analysis also suggests that domestic R&D capital stocks are the most important source of productivity growth with greater contribution to technological progress. China is likely to continue to expand its ODI and it will be interesting to see whether the productivity gains continue at the same rate, and whether other developing countries also increase their ODI and reap the same benefits.     

Discriminant Analysis Using Least Absolute Deviations

Several linear programming methods have been suggested as discrimination procedures. A least absolute deviations regression procedure is developed here which is simpler to use and does not suffer from any lack of invariance. A simulation study shows it to be at least as effective as any of the methods previously discussed for normal and heavy-tailed distributions.     

跨组织知识整合下的创新网络结构分析

针对跨组织合作形成的创新网络,构建了跨组织知识整合下的创新网络模型,仿真分析得出,组织间互补程度及知识整合效率对跨组织知识整合下的创新网络结构有一定的影响,具体表现为:组织间互补程度及知识整合效率很高时,创新网络具有较短的平均路径长度与较低的集聚系数,此时网络具有随机网络特征;组织间互补程度及知识整合效率很低时,创新网络具有较长的平均路径长度与较高的集聚系数,此时网络具有规则网络的特征;组织间互补程度及知识整合效率大小适度时,创新网络具有相对较短的平均路径长度与较高的集聚系数,因此创新网络具有"小世界"网络的特征。