Application of Multilevel Models to Structured Repeated Measurements

The linear mixed-effects model has been widely used for the analysis of continuous longitudinal data. This paper demonstrates that the linear mixed model can be adapted and used for the analysis of structured repeated measurements. A computational advantage of the proposed methodology is that there is no extra burden on the analyst since any software for linear mixed-effects models can be used to fit the proposed models. Two data sets from clinical psychology are used as motivating examples and to illustrate the methods.     

Estimation of a dynamic multi-level factor model with possible long-range dependence

A dynamic multi-level factor model with possible stochastic time trends is proposed. In the model, long-range dependence and short memory dynamics are allowed in global and local common factors as well as model innovations. Estimation of global and local common factors is performed on the prewhitened series, for which the prewhitening parameter is estimated semiparametrically from the cross-sectional and local average of the observable series. Employing canonical correlation analysis and a sequential least-squares algorithm on the prewhitened series, the resulting multi-level factor estimates have centered asymptotic normal distributions under certain rate conditions depending on the bandwidth and cross-section size. Asymptotic results for common components are also established. The selection of the number of global and local factors is discussed. The methodology is shown to lead to good small-sample performance via Monte Carlo simulations. The method is then applied to the Nord Pool electricity market for the analysis of price comovements among different regions within the power grid. The global factor is identified to be the system price, and fractional cointegration relationships are found between local prices and the system price, motivating a long-run equilibrium relationship. Two forecasting exercises are then discussed.     

Forecasting ATM cash demands using a local learning model of cerebellar associative memory network

Forecasting cash demands at automatic teller machines (ATMs) is challenging, due to the heteroskedastic nature of such time series. Conventional global learning computational intelligence (CI) models, with their generalized learning behaviors, may not capture the complex dynamics and time-varying characteristics of such real-life time series data efficiently. In this paper, we propose to use a novel local learning model of the pseudo self-evolving cerebellar model articulation controller (PSECMAC) associative memory network to produce accurate forecasts of ATM cash demands. As a computational model of the human cerebellum, our model can incorporate local learning to effectively model the complex dynamics of heteroskedastic time series. We evaluated the forecasting performance of our PSECMAC model against the performances of current established CI and regression models using the NN5 competition dataset of 111 empirical daily ATM cash withdrawal series. The evaluation results show that the forecasting capability of our PSECMAC model exceeds that of the benchmark local and global-learning based models.     

Transferability of disaggregate mode choice models

Predictions of aggregate transport mode split for inter-city trips are derived from a disaggregate model of travel demand. A series of tests are performed to assess the limitations of the prediction methodology for disaggregate models, and it is shown that disaggregate models are capable of predictions across diverse travel situations. The disaggregate model predictions are compared with predictions derived from aggregate models such as are currently used in urban transportation planning, and it is shown that disaggregate models based on much smaller data sets predict better than aggregate models while requiring no more information about the predicted population.     

基于网络分析法的条烟分拣时序快速算法模型

立足于条烟分拣系统的方案设计,根据条烟分拣设备的实际运行参数,借鉴工程管理中的网络时间分析计算方法,依托EM_Plant仿真平台建立仿真模型,通过理论分析和仿真模型验证,建立了条烟分拣关键时问参数的快速算法模型,为条烟分拣系统仿真及现场控制提供了一种实用方便的计算方法。     

Using difference equations to find optimal tax structures on the SugarScape

The use of equations to describe agent-based model dynamics allows access to mathematical theory that is not otherwise available. In particular, equation models can be effective at solving optimization problems—that is, problems concerning how an agent-based model can be most effectively steered into a particular state. In order to illustrate this strategy, we describe a modified version of the well-known SugarScape model and implement taxation. The optimization problem is to determine tax structures that minimize deaths but maximize tax income. Tax rates are dependent upon the amount of sugar available in a particular region; the rates change over time. A system of discrete difference equations is built to capture agent-based model dynamics. The equations are shown to capture the dynamics very well both with and without taxation. A multi-objective optimization technique known as Pareto optimization is then used to solve the problem. Rather than focusing on a cost function in which the two objectives are assigned weights, Pareto optimization is a heuristic method that determines a suite of solutions, each of which is optimal depending on the priorities of the researcher. In this case, Pareto optimization allows analysis of the tradeoff between taxes collected and deaths caused by taxation. The strategies contained here serve as a framework for a broad class of models.     

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.     

基于系统动态的学习型组织研究

学习型组织充分体现了知识经济时代对组织管理模式变化的要求,代表着组织未来的发展趋势。以系统动态学的视角,从组织学习、组织结构、系统要素三方面对学习型组织这个复杂系统进行了动态分析,分别建立了相应的动态反馈模型,揭示了学习型组织系统内部与其外部环境之间的动态反馈机制,提出了构建和完善学习型组织的建议。     

Automatic ARIMA modeling including interventions, using time series expert software

This article has three objectives: (a) to describe the method of automatic ARIMA modeling (AAM), with and without intervention analysis, that has been used in the analysis; (b) to comment on the results; and (c) to comment on the M3 Competition in general. Starting with a computer program for fitting an ARIMA model and a methodology for building univariate ARIMA models, an expert system has been built, while trying to avoid the pitfalls of most existing software packages. A software package called Time Series Expert TSE-AX is used to build a univariate ARIMA model with or without an intervention analysis. The characteristics of TSE-AX are summarized and, more especially, its automatic ARIMA modeling method. The motivation to take part in the M3-Competition is also outlined. The methodology is described mainly in three technical appendices: (Appendix A) choice of differences and of a transformation, use of intervention analysis; ( Appendix B) available specification procedures; ( Appendix C) adequacy, model checking and new specification. The problems raised by outliers are discussed, in particular how close they are from the forecast origin. Several series that are difficult to deal with from that point of view are mentioned and one of them is shown. In the last section, we comment on contextual information, the idea of an e−M Competition, prediction intervals and the possible use of other forecasting methods within Time Series Expert.     

Maximum likelihood estimation based on the Laplace approximation for p2 network regression models

The class of p₂ models is suitable for modeling binary relation data in social network analysis. A p₂ model is essentially a regression model for bivariate binary responses, featuring within‐dyad dependence and correlated crossed random effects to represent heterogeneity of actors. Despite some desirable properties, these models are used less frequently in empirical applications than other models for network data. A possible reason for this is due to the limited possibilities for this model for accounting for (and explicitly modeling) structural dependence beyond the dyad as can be done in exponential random graph models. Another motive, however, may lie in the computational difficulties existing to estimate such models by means of the methods proposed in the literature, such as joint maximization methods and Bayesian methods. The aim of this article is to investigate maximum likelihood estimation based on the Laplace approximation approach, that can be refined by importance sampling. Practical implementation of such methods can be performed in an efficient manner, and the article provides details on a software implementation using R . Numerical examples and simulation studies illustrate the methodology.     

Estimation of parameters in mathematical models of segregation

Several differential equations models have been proposed which describe transitions of neighborhoods from predominantly white to predominantly non-white. We show how to fit such models to data by estimating parameters and initial conditions. The mathematical and computational tool, quasilinearization, is shown to work well on a model due to Levine.     

The shale revolution and shifting crude dynamics

Oil price fluctuates in response to both demand and supply shocks. This paper proposes a new methodology that allows for timely identification of the shifting contribution from the two types of shock through a joint analysis of crude futures options and stock index options. Historical analysis shows that crude oil price movements are dominated by supply shocks from 2004 to 2008, but demand shocks have become much more dominant since then. The large demand shock following the 2008 financial crisis contributes to the start of this dynamics shift, whereas the subsequent shale revolution has fundamentally altered the crude supply behavior.     

Stochastic models of resonating markets

This paper describes a way to model a seasonally and irregularly peaking price dynamics, as that originated in commodity and energy markets, using a system of coupled nonlinear stochastic differential equations. The specific case of an electric power market is used to show which microeconomic features this approach is able to model. Critical point analysis is used in a simple way to show how the interaction between dynamic criticality and stochasticity can be used to develop further models, useful to explore more deeply other types of peaking price dynamics.     

Changing energy parameters and the evolution of urban spatial structure

In this paper two different models of urban spatial structure are developed. The first is based on equilibrium analysis and the dynamics of the equilibrium solution are examined. The second is based on disequilibrium analysis and is formulated as a set of differential equations. The models are run to assess the effects of changing energy parameters, particularly in relation to transport costs, and it is shown that a range of different patterns of spatial structure emerge.     

Price formation in commodity markets

This paper develops a theory-consistent market model for storable commodities and illustrates its characterization of the data-generating process for a set of major traded commodities. The dynamics of the system incorporate recent advances in modelling techniques. Cointegrated variables in the demand functions are represented by the error correction mechanism (ECM), and expected prices in the stock demand relationship are generated by a rational expectations process. The outside-sample performance of the model is tested against the pure time-series model used to formulate expected prices, and is shown to have a smaller mean square error than that of the time-series model. Thus the model provides comparatively efficient forecasts and, unlike models constructed in their reduced form, permits consideration of key behavioural relationships in commodity markets.     

Diagnostics analysis for log‐Birnbaum–Saunders regression models with censored data

This article develops influence diagnostics for log‐Birnbaum–Saunders (LBS) regression models with censored data based on case‐deletion model (CDM). The one‐step approximations of the estimates in CDM are given and case‐deletion measures are obtained. Meanwhile, it is shown that CDM is equivalent to mean shift outlier model (MSOM) in LBS regression models and an outlier test is presented based on MSOM. Furthermore, we discuss a score test for homogeneity of shape parameter in LBS regression models. Two numerical examples are given to illustrate our methodology and the properties of score test statistic are investigated through Monte Carlo simulations under different censoring percentages.     

Bayesian semiparametric stochastic volatility modeling

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, nonparametric Bayesian methods are used to flexibly model the skewness and kurtosis of the distribution while the dynamics of volatility continue to be modeled with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. A Markov chain Monte Carlo sampling approach to estimation is presented with theoretical and computational issues for simulation from the posterior predictive distributions. An empirical example compares the new model to standard parametric stochastic volatility models.     

Performance evaluation of reverse logistics in food supply chains in a circular economy using system dynamics

Supply chains are composed of multiple stakeholders who have complex interrelationships. In addition, the forward and reverse flow of materials, information, human resources, and finance occurs among different stakeholders in closing the loop of supply chains. Reverse logistics (RL) activities are gaining importance in terms of size and quantity due to both economic and environmental concerns. These flows in RL in supply chains are both dynamic and complex in nature. Further, the environmental impact of RL activities has barely been considered in holistic way in available literature. In this study, a system dynamics model has been developed to analyze and comprehend the green performance of RL activities by predicting the environmental impact of RL activities. The proposed model has been validated by a case study in the context of a food supply chain. In the company where the case study is carried out, the environmental effects of RL activities have been analyzed. These activities in a food supply chain in terms of CO₂ (carbon dioxide), NO_x (nitrogen oxide), SO₂ (sulfur dioxide), and PM (particulate matter) emissions have been predicted through a system dynamics model for the years 2020 to 2024. The proposed methodology is applied in a food supply context, a major player in retail business, especially in emerging economies. According to our findings, the RL activities in a food supply chain can significantly contribute to green performance management by minimizing food waste and loss; hence, the environmental impacts of such activities should be closely examined from a managerial perspective.     

Horseshoe Regularisation for Machine Learning in Complex and Deep Models1

Since the advent of the horseshoe priors for regularisation, global–local shrinkage methods have proved to be a fertile ground for the development of Bayesian methodology in machine learning, specifically for high-dimensional regression and classification problems. They have achieved remarkable success in computation and enjoy strong theoretical support. Most of the existing literature has focused on the linear Gaussian case; for which systematic surveys are available. The purpose of the current article is to demonstrate that the horseshoe regularisation is useful far more broadly, by reviewing both methodological and computational developments in complex models that are more relevant to machine learning applications. Specifically, we focus on methodological challenges in horseshoe regularisation in non-linear and non-Gaussian models, multivariate models and deep neural networks. We also outline the recent computational developments in horseshoe shrinkage for complex models along with a list of available software implementations that allows one to venture out beyond the comfort zone of the canonical linear regression problems.     

Exchange rate predictability and dynamic Bayesian learning

We consider how an investor in the foreign exchange market can exploit predictive information by means of flexible Bayesian inference. Using a variety of vector autoregressive models, the investor is able, each period, to learn about important data features. The developed methodology synthesizes a wide array of established approaches for modeling exchange rate dynamics. In a thorough investigation of monthly exchange rate predictability for 10 countries, we find that using the proposed methodology for dynamic asset allocation achieves substantial economic gains out of sample. In particular, we find evidence for sparsity, fast model switching, and exploitation of the exchange rate cross-section.