Bridging the Gap between Different Statistical Approaches: An Integrated Framework for Modelling

This paper proposes a template for modelling complex datasets that integrates traditional statistical modelling approaches with more recent advances in statistics and modelling through an exploratory framework. Our approach builds on the well-known and long standing traditional idea of 'good practice in statistics' by establishing a comprehensive framework for modelling that focuses on exploration, prediction, interpretation and reliability assessment, a relatively new idea that allows individual assessment of predictions.
The integrated framework we present comprises two stages. The first involves the use of exploratory methods to help visually understand the data and identify a parsimonious set of explanatory variables. The second encompasses a two step modelling process, where the use of non-parametric methods such as decision trees and generalized additive models are promoted to identify important variables and their modelling relationship with the response before a final predictive model is considered. We focus on fitting the predictive model using parametric, non-parametric and Bayesian approaches.
This paper is motivated by a medical problem where interest focuses on developing a risk stratification system for morbidity of 1,710 cardiac patients given a suite of demographic, clinical and preoperative variables. Although the methods we use are applied specifically to this case study, these methods can be applied across any field, irrespective of the type of response.     

Modeling demographic-economic interactions: Micro, macro and linked micro/macro strategies

The inherent limitations of group-based macro modeling preclude a realistis behavioral representation of regional demographic-economic interactions. Such a harsh assessment is unwelcome news to regional modelers, who for the most part have been relying on group-based macro strategies. This paper addresses the issue of choosing the most appropriate approach to regional demographic-economic modeling by emphasizing once again the severe limitations of group-based macro models for representing population behavior, advocating instead the use of sample-based micro models for representing population behavior and suggesting that a linked macro/micro strategy for modeling demographic-economic interactions might combine the advantages of both modeling methodologies.     

Dynamic decision-making for reliability and maintenance analysis of manufacturing systems based on failure effects

A framework for reliability and maintenance analysis of job shop manufacturing systems is proposed in this paper. An efficient preventive maintenance (PM) policy in terms of failure effects analysis (FEA) is proposed. Subsequently, reliability evaluation and component importance measure based on FEA are performed under the PM policy. A job shop manufacturing system is applied to validate the reliability evaluation and dynamic maintenance policy. Obtained results are compared with existed methods and the effectiveness is validated. Some vague understandings for issues such as network modelling, vulnerabilities identification, the evaluation criteria of repairable systems, as well as PM policy during manufacturing system reliability analysis are elaborated. This framework can help for reliability optimisation and rational maintenance resources allocation of job shop manufacturing systems.     

Choice Modelling Approaches: A Superior Alternative for Environmental Valuatioin?

In this paper, we examine some popular 'choice modelling' approaches to environmental valuation, which can be considered as alternatives to more familiar valuation techniques based on stated preferences such as the contingent valuation method. A number of choice modelling methods are consistent with consumer theory, and its focus on an attribute‐based theory of value permits a superior representation of many environmental management contexts. However, choice modelling surveys can place a severe cognitive burden upon respondents and induce satisficing rather than maximising behavioural patterns. In this framework, we seek to identify the best available choice modelling alternative and investigate its potential to 'solve' some of the major biases associated with standard contingent valuation. We then discuss its use in the light of policy appraisal needs within the EU. An application to the demand for rock climbing in Scotland is provided as an illustration.     

A unified approach to nonlinearity,structural change,and outliers

This paper demonstrates that the class of conditionally linear and Gaussian state-space models offers a general and convenient framework for simultaneously handling nonlinearity, structural change and outliers in time series. Many popular nonlinear time series models, including threshold, smooth transition and Markov-switching models, can be written in state-space form. It is then straightforward to add components that capture parameter instability and intervention effects. We advocate a Bayesian approach to estimation and inference, using an efficient implementation of Markov Chain Monte Carlo sampling schemes for such linear dynamic mixture models. The general modelling framework and the Bayesian methodology are illustrated by means of several examples. An application to quarterly industrial production growth rates for the G7 countries demonstrates the empirical usefulness of the approach.     

THE DISTRIBUTION OF WEALTH: MEASUREMENT AND MODELS

Abstract. Analysts debating the consequences of a policy change for the wealth distribution may come to different conclusions because of different views about how the distribution should be defined and measured, or about the processes determining the distribution. The aim of this survey is to provide an analytical framework within which such conflicts may be assessed. The first part of the paper discusses conceptual issues in the definition of 'wealth', and compares methods of deriving estimates of wealth distribution. The second, and larger, part of the paper surveys lifecycle and intergenerational models of the distribution of wealth, including a discussion of the role played by inheritance. The presentation is largely theoretical. Indeed, one of the paper's conclusions is that empirical modelling of the wealth distribution is under-developed, at least for the purposes of addressing many topical policy issues.     

Industry-oriented enterprise resource planning

General purpose enterprise resource planning (ERP) systems have problems such as complex configuration processes, low adaptation to specific industries, and an extensive implementation time period. In order to alleviate these issues, this paper proposes the concept of industry-oriented ERP (IERP) and a component-based approach to IERP development. This paper also presents the framework of developing IERP in which business process modelling and software reuse are employed as primary methods to improve the operability of component-based IERP. The framework consists of five layers including server layer, team work supporting layer, IERP construction and customisation layer, reusable assets and toolset layer, and the IERP system instance layer. These layers as well as their relationship with each other are also discussed.     

Multicriteria framework for selecting a process modelling language

The choice of process modelling language can affect business process management (BPM) since each modelling language shows different features of a given process and may limit the ways in which a process can be described and analysed. However, choosing the appropriate modelling language for process modelling has become a difficult task because of the availability of a large number modelling languages and also due to the lack of guidelines on evaluating, and comparing languages so as to assist in selecting the most appropriate one. This paper proposes a framework for selecting a modelling language in accordance with the purposes of modelling. This framework is based on the semiotic quality framework (SEQUAL) for evaluating process modelling languages and a multicriteria decision aid (MCDA) approach in order to select the most appropriate language for BPM. This study does not attempt to set out new forms of assessment and evaluation criteria, but does attempt to demonstrate how two existing approaches can be combined so as to solve the problem of selection of modelling language. The framework is described in this paper and then demonstrated by means of an example. Finally, the advantages and disadvantages of using SEQUAL and MCDA in an integrated manner are discussed.     

Inference on stochastic time-varying coefficient models

Recently, there has been considerable work on stochastic time-varying coefficient models as vehicles for modelling structural change in the macroeconomy with a focus on the estimation of the unobserved paths of random coefficient processes. The dominant estimation methods, in this context, are based on various filters, such as the Kalman filter, that are applicable when the models are cast in state space representations. This paper introduces a new class of autoregressive bounded processes that decompose a time series into a persistent random attractor, a time varying autoregressive component, and martingale difference errors. The paper examines, rigorously, alternative kernel based, nonparametric estimation approaches for such models and derives their basic properties. These estimators have long been studied in the context of deterministic structural change, but their use in the presence of stochastic time variation is novel. The proposed inference methods have desirable properties such as consistency and asymptotic normality and allow a tractable studentization. In extensive Monte Carlo and empirical studies, we find that the methods exhibit very good small sample properties and can shed light on important empirical issues such as the evolution of inflation persistence and the purchasing power parity (PPP) hypothesis.     

Daily peak electrical load forecasting with a multi-resolution approach

In the context of smart grids and load balancing, daily peak load forecasting has become a critical activity for stakeholders in the energy industry. An understanding of peak magnitude and timing is paramount for the implementation of smart grid strategies such as peak shaving. The modelling approach proposed in this paper leverages high-resolution and low-resolution information to forecast daily peak demand size and timing. The resulting multi-resolution modelling framework can be adapted to different model classes. The key contributions of this paper are (a) a general and formal introduction to the multi-resolution modelling approach, (b) a discussion of modelling approaches at different resolutions implemented via generalised additive models and neural networks, and (c) experimental results on real data from the UK electricity market. The results confirm that the predictive performance of the proposed modelling approach is competitive with that of low- and high-resolution alternatives.     

Advances in Joint Modelling: A Review of Recent Developments with Application to the Survival of End Stage Renal Disease Patients

The use of joint modelling approaches is becoming increasingly popular when an association exists between survival and longitudinal processes. Widely recognized for their gain in efficiency, joint models also offer a reduction in bias compared with naïve methods. With the increasing popularity comes a constantly expanding literature on joint modelling approaches. The aim of this paper is to give an overview of recent literature relating to joint models, in particular those that focus on the time‐to‐event survival process. A discussion is provided on the range of survival submodels that have been implemented in a joint modelling framework. A particular focus is given to the recent advancements in software used to build these models. Illustrated through the use of two different real‐life data examples that focus on the survival of end‐stage renal disease patients, the use of the JM and joineR packages within R are demonstrated. The possible future direction for this field of research is also discussed.     

A model of fractional cointegration, and tests for cointegration using the bootstrap

The paper proposes a framework for modelling cointegration in fractionally integrated processes, and considers methods for testing the existence of cointegrating relationships using the parametric bootstrap. In these procedures, ARFIMA models are fitted to the data, and the estimates used to simulate the null hypothesis of non-cointegration in a vector autoregressive modelling framework. The simulations are used to estimate p-values for alternative regression-based test statistics, including the F goodness-of-fit statistic, the Durbin–Watson statistic and estimates of the residual d. The bootstrap distributions are economical to compute, being conditioned on the actual sample values of all but the dependent variable in the regression. The procedures are easily adapted to test stronger null hypotheses, such as statistical independence. The tests are not in general asymptotically pivotal, but implemented by the bootstrap, are shown to be consistent against alternatives with both stationary and nonstationary cointegrating residuals. As an example, the tests are applied to the series for UK consumption and disposable income. The power properties of the tests are studied by simulations of artificial cointegrating relationships based on the sample data. The F test performs better in these experiments than the residual-based tests, although the Durbin–Watson in turn dominates the test based on the residual d.     

The role of Embeddedness in Industrial Symbiosis Networks: Phases in the Evolution of Industrial Symbiosis Networks

Industrial symbiosis (IS) has emerged as a body of exchange structures to progress to a more eco‐efficient industrial system, by establishing a collaborative web of knowledge, material and energy exchanges among different organizational units. However, even given the potential economic and environmental benefits derived from IS networks, the process of emergence and development of these networks seems far from straightforward. The effective operation of such networks relies heavily on aspects such as trust and general reciprocity, aspects insufficiently covered in the IS literature. Based on the theoretical framework provided by social network analysis and economic geography and the empirical data collected through qualitative methods, based on the approach of Grounded Theory, the authors propose a modelling framework to analyse the main mechanisms in the building of trust and embeddedness and identify different phases in cooperation leading to effective IS exchanges. Copyright © 2010 John Wiley & Sons, Ltd and ERP Environment.     

On domain modelling of the service system with its application to enterprise information systems

Information systems are a kind of service systems and they are throughout every element of a modern industrial and business system, much like blood in our body. Types of information systems are heterogeneous because of extreme uncertainty in changes in modern industrial and business systems. To effectively manage information systems, modelling of the work domain (or domain) of information systems is necessary. In this paper, a domain modelling framework for the service system is proposed and its application to the enterprise information system is outlined. The framework is defined based on application of a general domain modelling tool called function-context-behaviour-principle-state-structure (FCBPSS). The FCBPSS is based on a set of core concepts, namely: function, context, behaviour, principle, state and structure and system decomposition. Different from many other applications of FCBPSS in systems engineering, the FCBPSS is applied to both infrastructure and substance systems, which is novel and effective to modelling of service systems including enterprise information systems. It is to be noted that domain modelling of systems (e.g. enterprise information systems) is a key to integration of heterogeneous systems and to coping with unanticipated situations facing to systems.     

Demographic-economic analysis in a multi-zonal region: a case study of Nordrhein-Westfalen

"This paper develops a series of spatially-disaggregated demographic-economic models, based on an extended Leontief type input-output model. Travel between residence and place of work and residence and place of shopping is included in the models, and a detailed decomposition of a range of multipliers identified. A model explicitly involving travel to work is developed empirically for the state of Nordrhein-Westfalen in West Germany, and sets of multipliers presented which relate changes in demographic and economic variables in particular zones of the state with changes in economic output and employment-related variables in other zones."     

Threshold Effects in Cointegrating Relationships*

In this paper, we introduce threshold‐type nonlinearities within a single‐equation cointegrating regression model and propose a testing procedure for testing the null hypothesis of linear cointegration vs. cointegration with threshold effects. Our framework allows the modelling of long‐run equilibrium relationships that may change according to the magnitude of a threshold variable assumed to be stationary and ergodic, and thus constitutes an attempt to deal econometrically with the potential presence of multiple equilibria. The framework is flexible enough to accommodate regressor endogeneity and serial correlation.     

Incorporating sustainability indicators into a computable general equilibrium model of the scottish economy

In recent years, the notion of sustainable development has begun to figure prominently in the regional, as well as the national, policy concerns of many industrialized countries. Indicators have typically been used to monitor changes in economic, environmental and social variables to show whether economic development is on a sustainable path. In this paper we endogenize individual and composite environmental indicators within an appropriately specified computable general equilibrium modelling framework for Scotland. In principle, at least, this represents a very powerful modelling tool that can inform the policy making process by identifying the impact of any exogenous policy change on the key endogenous environmental and economic indicators. It can also identify the effects of any binding environmental targets on economic activity.     

Statistical Network Analysis: A Review with Applications to the Coronavirus Disease 2019 Pandemic

As the coronavirus disease 2019 outbreak evolves, statistical network analysis is playing an essential role in informing policy decisions. Therefore, researchers who are new to such studies need to understand the techniques available to them. As a field, statistical network analysis aims to develop methods that account for the complex dependencies found in network data. Over the last few decades, the area has rapidly accumulated methods, including techniques for network modelling and simulating the spread of infectious disease. This article reviews these network modelling techniques and their applications to the coronavirus disease 2019 pandemic.