Statistical Implementations of Agent-Based Demographic Models

A variety of demographic statistical models exist for studying population dynamics when individuals can be tracked over time. In cases where data are missing due to imperfect detection of individuals, the associated measurement error can be accommodated under certain study designs (e.g. those that involve multiple surveys or replication). However, the interaction of the measurement error and the underlying dynamic process can complicate the implementation of statistical agent-based models (ABMs) for population demography. In a Bayesian setting, traditional computational algorithms for fitting hierarchical demographic models can be prohibitively cumbersome to construct. Thus, we discuss a variety of approaches for fitting statistical ABMs to data and demonstrate how to use multi-stage recursive Bayesian computing and statistical emulators to fit models in such a way that alleviates the need to have analytical knowledge of the ABM likelihood. Using two examples, a demographic model for survival and a compartment model for COVID-19, we illustrate statistical procedures for implementing ABMs. The approaches we describe are intuitive and accessible for practitioners and can be parallelised easily for additional computational efficiency.     

电工电子学教学改革的探索与实践——论四段式教学模式

本文提出了关于电工学实行"四段式"教学模式的设想。四段式教学是指:感性认识阶段、理论教学阶段、实践教学阶段、综合提高阶段。通过这种四段式教学体系的初步实践及教学效果的反馈,深刻体会到这种教学模式能够进一步开发学生的创造性思维,培养学生的动手能力,引导学生培养自学能力。让学生在学习理论知识前学会提出问题,在学习理论知识后学会发现新问题,从而能够通过自学不断的将新知识填充到自己的知识框架中,这样学生就掌握了科学研究的基本方法(提出问题-解决问题-提出新问题),具备了很强的适应能力和可持续发展的能力,将终身受益。     

Inference with non-Gaussian Ornstein–Uhlenbeck processes for stochastic volatility

Continuous-time stochastic volatility models are becoming an increasingly popular way to describe moderate and high-frequency financial data. Barndorff-Nielsen and Shephard (2001a) proposed a class of models where the volatility behaves according to an Ornstein–Uhlenbeck (OU) process, driven by a positive Lévy process without Gaussian component. These models introduce discontinuities, or jumps, into the volatility process. They also consider superpositions of such processes and we extend that to the inclusion of a jump component in the returns. In addition, we allow for leverage effects and we introduce separate risk pricing for the volatility components. We design and implement practically relevant inference methods for such models, within the Bayesian paradigm. The algorithm is based on Markov chain Monte Carlo (MCMC) methods and we use a series representation of Lévy processes. MCMC methods for such models are complicated by the fact that parameter changes will often induce a change in the distribution of the representation of the process and the associated problem of overconditioning. We avoid this problem by dependent thinning methods. An application to stock price data shows the models perform very well, even in the face of data with rapid changes, especially if a superposition of processes with different risk premiums and a leverage effect is used.     

Multilevel Analysis of Repeated Measures Data

Hierarchically structured data are common in many areas of scientific research. Such data are characterized by nested membership relations among the units of observation. Multilevel analysis is a class of methods that explicitly takes the hierarchical structure into account. Repeated measures data can be considered as having a hierarchical structure as well: measurements are nested within, for instance, individuals. In this paper, an overview is given of the multilevel analysis approach to repeated measures data. A simple application to growth curves is provided as an illustration. It is argued that multilevel analysis of repeated measures data is a powerful and attractive approach for several reasons, such as flexibility, and the emphasis on individual development.     

AFT regression-adjusted monitoring of reliability data in cascade processes

Today’s competitive market has witnessed a growing interest in improving the reliability of products in both service and industrial operations. A large number of monitoring schemes have been introduced to effectively control the reliability-related quality characteristics. These methods have focused on single-stage processes or considered quality variables which are independent. However, the main feature of multistage processes is the cascade property which needs to be justified for the sake of optimal process monitoring. The problem becomes complicated when the presence of censored observations is pronounced. Therefore, both the effects of influential covariates and censored data must be taken into account while presenting a monitoring scheme. In this paper, the accelerated failure time models are used and two regression-adjusted control schemes based on Cox-Snell residuals are devised. Two different scenarios with censored and non-censored data are considered respectively. The competing control charts are compared in terms of zero-state and steady-state average run length criteria using Markov chain approach. The comparison study reveals that the cumulative sum based monitoring procedure is superior and more effective. It should be noted that the application of the proposed monitoring schemes are not restricted to manufacturing processes and thus service operations such as healthcare systems can benefit from them.     

Spatio-temporal modeling of yellow taxi demands in New York City using generalized STAR models

The spatio-temporal variation in the demand for transportation, particularly taxis, in the highly dynamic urban space of a metropolis such as New York City is impacted by various factors such as commuting, weather, road work and closures, disruptions in transit services, etc. This study endeavors to explain the user demand for taxis through space and time by proposing a generalized spatio-temporal autoregressive (STAR) model. It deals with the high dimensionality of the model by proposing the use of LASSO-type penalized methods for tackling parameter estimation. The forecasting performance of the proposed models is measured using the out-of-sample mean squared prediction error (MSPE), and the proposed models are found to outperform other alternative models such as vector autoregressive (VAR) models. The proposed modeling framework has an easily interpretable parameter structure and is suitable for practical application by taxi operators. The efficiency of the proposed model also helps with model estimation in real-time applications.     

项目分包质量管理的策略与方法

项目分包是一种重要的生产合作方式,已为越来越多的企业所采用。研究项目分包质量管理的策略与方法,具有重要的理论和实践意义。本文首先根据项目分包的特点与分包过程,提出了项目分包质量管理的三阶段策略;其次,应用分包管理理论、项目质量管理方法,研究了项目分包各阶段质量管理的方法。     

The effect of spatiotemporal resolution on predictive policing model performance

Being able to anticipate crime such that new crime events can be dealt with effectively or prevented entirely, leads police forces worldwide to look at applying predictive policing, which provides predictions of times and places at risk for crime, such that proactive preventative measures can be taken. Ideally, predictive policing models predict crime at a high spatio-temporal level, while also providing optimal prediction performance. The main objective of this paper is therefore to evaluate the impact of varying grid resolution, temporal resolution and historical time frame on prediction performance. To investigate this, we analyse home burglary data from a large city in Belgium and predict new crime events using a range of parameter values, comparing the resulting prediction performances. Given the potential prediction performance costs associated with prediction at a high spatio-temporal resolution, consideration should be given to balance practical requirements with performance requirements.     

Towards a Socio-Cognitive Approach to Knowledge Transfer

abstract    Dominant research streams in the knowledge transfer field, such as the positivist and social constructionist approaches, largely assume that knowledge transfer is accomplished through instructions and/or socially constructed practices. Underlying these views is the belief that texts and practices carry with them the codes necessary for their own decoding and therefore enable an unproblematic knowledge transfer. In contrast, we argue that the decoding of information into meaningful knowledge is always mediated by people's private and cultural models, which are created from the unique combination of their cognitive dispositions (i.e. acumen, memory, creativity, volitions, emotions) and socio-cultural interaction. The degree to which people apply these models reflectively and/or categorically (i.e. automatically) depends on the need for cognition as well as environmental demands and feedback. Therefore, knowledge transfer is always tentative, because it depends on the application of private and cultural models along the continuum that goes from reflective to categorical processing. We present first a critique of the positivist and social constructionist positions; then we introduce a socio-cognitive model that captures and explicates socio-cognitive processes involved in sense making during knowledge transfer. Finally, we explore future research streams and managerial implications.     

Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting

Wind power forecasts with lead times of up to a few hours are essential to the optimal and economical operation of power systems and markets. Vector autoregression (VAR) is a framework that has been shown to be well suited to predicting for several wind farms simultaneously by considering the spatio-temporal dependencies in their time series. Lasso penalisation yields sparse models and can avoid overfitting the large numbers of coefficients in higher dimensional settings. However, estimation in VAR models usually does not account for changes in the spatio-temporal wind power dynamics that are related to factors such as seasons or wind farm setup changes, for example. This paper tackles this problem by proposing a time-adaptive lasso estimator and an efficient coordinate descent algorithm for updating the VAR model parameters recursively online. The approach shows good abilities to track changes in the multivariate time series dynamics on simulated data. Furthermore, in two case studies it shows clearly better predictive performances than the non-adaptive lasso VAR and univariate autoregression.     

Understanding and classifying the role of design demonstrators in scientific exploration

This paper describes the development of a model for classifying the different type of ‘design demonstrator’ that might be used in translating scientific activity from the laboratory to the market. Two detailed case studies are described in which designers worked closely with scientists. In one of the projects, the scientists were seeking to commercialise their research. In the other, the research was at an early stage and the scientists had not considered commercialisation. Different types of physical artefact produced in these collaborative projects were analysed to identify the extent to which they might contribute to science, technology, application or market. Evidence indicates that demonstrators might fulfil multiple purposes and that the translation from science to market is more complicated than is often shown in linear models. An original classification of the role of demonstrators through this journey is provided.     

The evolution of environmental management: from stage models to performance evaluation

In the past two decades, academics and practitioners have attempted to improve understanding of environmental management by classifying companies' environmental behaviour, and evaluating their performance. Driven by both research and societal interest, this has resulted in a wave of stage or phase models, and a range of typologies. This article gives an overview of the development of such environmental management models, analysing their characteristics, strengths and weaknesses. An evolution can be noted in the direction of typologies and non‐linear models to deal with organizational and strategic complexities. Models are starting to pay more attention to the management side. To overcome problems of operationalization and limited company and sector specificity, environmental performance evaluation systems have emerged more recently. Although comprehensive performance assessments are still unavailable, the tenets of such a system can already be delineated. The paper presents these components, and draws conclusions on the contribution of environmental management models and performance evaluation systems. Copyright © 2002 John Wiley & Sons, Ltd. and ERP Environment     

Pre-defined and optional staging for the deployment of enterprise systems: a case study and a framework

The effective deployment of enterprise systems has been a major challenge for many organisations. Customising the new system, changing business processes, and integrating multiple information sources are all difficult tasks. As such, they are typically done in carefully planned stages in a process known as phased implementation. Using ideas from Option Theory, this article critiques aspects of phased implementation. One customer relationship management (CRM) project and its phased implementation are described in detail and ten other enterprise system deployments are summarised as a basis for the observation that almost all deployment stages are pre-defined operational steps rather than decision points. However, Option Theory suggests that optional stages, to be used only when risk materialises, should be integral parts of project plans. Although such optional stages are often more valuable than pre-defined stages, the evidence presented in this article shows that they are only rarely utilised. Therefore, a simple framework is presented; it first identifies risks related to the deployment of enterprise systems, then identifies optional stages that can mitigate these risks, and finally compares the costs and benefits of both pre-defined and optional stages.     

工程项目管理虚拟组织的构建及运行

建筑工程项目管理是一项复杂的系统工程，面对知识经济的挑战，针对当前的建筑工程项目应如何进行科学管理问题，提出应利用虚拟组织的形式，借助网络平台在工程项目管理中引入知识管理，通过管理各方知识的交流、整合、优化、应用，实现建筑工程整体化，力争以最小的成本最佳的完成项目的建设目标。     

Text Analysis for Knowledge Graphs

The concept of knowledge graphs is introduced as a method to represent the state of the art in a specific scientific discipline. Next the text analysis part in the construction of such graphs is considered. Here the ‘translation’ from text to graph takes place. The method that is used here is compared to methods used in other approaches in which texts are analysed.     

Ranking Economics Departments in a Contested Discipline: A Bibliometric Approach to Quality Equality Between Theoretically Distinct Subdisciplines

Quality ranking of economic journals and departments is a widespread practice in the United States. The methods used are peer review and bibliometric measures. In a divided discipline such as economics scientific knowledge is contested. So knowing which journals and departments are the best in terms of research is somewhat muddied. If the methods used to measure the production of quality scientific knowledge are tilted towards one of the contested approaches, the resulting quality rankings of journals and departments are tilted as well. So if the objective is the open‐minded pursuit of the production of scientific knowledge, then it is important to have measures of quality that treat the different contested approaches equally. Our article explores this issue by examining the impact that a quality‐equality bibliometric measure can have on the quality rankings of doctoral economic programs in the United States.     

Selecting and fitting graphical chain models to longitudinal data

The aim of this paper is to demonstrate how graphical chain models can be used as effective tools in life course research focusing in particular on models for longitudinal prospective data. The substantive research question focuses on whether young motherhood is a pathway through which socio-economic disadvantage in childhood is related to poor self-reported health in adulthood among the 1970 British birth cohort. By breaking down large multivariate systems into simpler more tractable subcomponents and analysing them via local regressions, graphical models help the understanding of complicated life course processes, show the intermediate relationships between predictors, and aid the understanding of the mechanisms through which potential confounding and mediating factors affect the outcome of interest.     

An Investigation of Industry Associations,Association Loops and Economic Complexity: Application to Canada and the United States

Various methods were proposed to understand the linkages in an input-output system; however, many have focused only on the identification of key sectors in the economy. An alternative approach, identifying analytically the importance of elements and combinations of elements was proposed as a field of influence theory (Sonis et al. , 1996). The purpose of this paper is to offer a complementary approach to the field of influence and the so-called 'Matrioshka principal' (Sonis & Hewings, 1990); the objectives are to identify simple row-column associations (i.e. statistical dependence), seek hierarchical associations between supply and demand in input-output systems and the decomposition of economic complexity into finite stages. For the identification of simple dependencies between rows and columns, we use a log-linear regression and for hierarchical associations and the identification of complexity stages, we use the data analysis technique known as dual scaling. Results of both approaches will be applied to input-output tables of the US and Canada.     

Composition versus decomposition in two-stage network DEA: a reverse approach

A two-stage production process assumes that the first stage transforms external inputs to a number of intermediate measures, which then are used as inputs to the second stage that produces the final outputs. The fundamental approaches to two-stage network data envelopment analysis are the multiplicative and the additive efficiency-decomposition approaches. Both they assume a series relationship between the two stages but they differ in the definition of the overall system efficiency as well as in the way they conceptualize the decomposition of the overall efficiency to the efficiencies of the individual stages. In this paper, we first show that the efficiency estimates obtained by the additive decomposition method are biased, by unduly favouring one stage against the other, while those obtained by the multiplicative method are not unique. Then, we present a novel approach to estimate unique and unbiased efficiency scores for the individual stages, which are then composed to obtain the efficiency of the overall system, by selecting the aggregation method a posteriori. Within the particularity of two-stage processes emerging from the conflicting role of the intermediate measures, we develop an envelopment model to locate the efficient frontier whose derivation from our primal multiplier efficiency assessment model is effectively justified. The results derived from our approach are compared with those obtained by the aforementioned basic methods on experimental data as well as on test data drawn from the literature. Similarities and dissimilarities in the results are rigorously justified.     

Asymptotically optimal tests for some growth curve models under non-normal error structure

This paper considers growth curve models consisting of two stages. In the first stage we have a regression model with random parameter. The second stage is given by a linear model for the mean of the random parameter of the first stage. In the present paper we prove asymptotic optimality of tests for linear functions of the mean of the random parameter under non-normal error. Received: September 1999