General Model Formulation Research Articles

Extension of the Random-Effects Regression Algorithm to Account for the Effects of Nonlinear Site Response

Abstract The random-effects regression algorithm, made popular within engineering seismology by Abrahamson and Youngs (1992), is arguably the most commonly used approach for developing empirical ground-motion models. The original presentation of this algorithm relates to the most simple application of a far more general mixed-effects model formulation. In recent years, it has become increasingly common to incorporate nonlinear site response effects within empirical, or semi-empirical, ground-motion models, but the original random-effects algorithm does not apply to cases in which the random effects enter the model in a nonlinear manner. This article presents a more general algorithm for fitting mixed-effects models that can accommodate nonlinear site effects (among other effects). The presented algorithm deliberately mirrors that of Abrahamson and Youngs (1992) but allows for the treatment of far more elaborate variance structures.

Environmental Education, a Way to Introduce and Improve Urban Environmental Pollution

Background: In this study, high school students in Tehran were selected to investigate the role of environmental education in the prevention and control of pollution. Objectives: In this regard, 10 high schools from different areas of Tehran City were used to implement an Environmental Education (EE) intervention program. Patients and Methods: The students of 5 high schools (first group) were trained by the paper-based guidebook and students of other 5 high schools (second group) trained by a teacher (EE expert). This study has been designed as a quasi-experimental pretest/posttest design with comparison groups. The impact on the participant’s environmental literacy was assessed by measuring a number of environmental literacy components, including knowledge, attitude, values, and concerns as well as behavioral intention/behavior. The results were presented in a bipolar 5-point Likert response format, with an undecided category. Relationships among variables were examined using the general linear model formulation with subsequent ANOVA analyses. Results: Results for the participants show a significant increase in the scores of knowledge and attitude about air pollution. Educational program and training tools had no effect on the concern indexes, values, and behavior toward air pollution. Review of the score changes toward waste component, shows that posttest scores (in comparison with pretest ones) increases in 4 indicators of knowledge, attitude, concern, and behavior, however the value index did not change so much. Conclusions: We can conclude that educational program can be used to convey the knowledge of the environment and in this regard, its implementation is necessary.

Hierarchical Kendall copulas: Properties and inference

AbstractWhile there is substantial need for dependence models in higher dimensions, most existing models quickly become rather restrictive and barely balance parsimony and flexibility. Hierarchical constructions may improve on that by grouping variables in different levels. In this paper, the new class of hierarchical Kendall copulas is proposed and discussed. Hierarchical Kendall copulas are built up by flexible copulas specified for groups of variables, where aggregation is facilitated by the Kendall distribution function, the multivariate analog to the probability integral transform for univariate random variables. After deriving properties of the general model formulation, particular focus is given to inference techniques of hierarchical Kendall copulas with Archimedean components, for which closed‐form analytical expressions can be derived. A substantive application to German stock returns finally shows that hierarchical Kendall copulas perform very well for real data, out‐of‐ as well as in‐sample. The Canadian Journal of Statistics 42: 78–108; 2014 © 2014 Statistical Society of Canada

Fuzzy model xml formulation for production dynamics analysis and control

The paper introduces an XML schema complying with PMML standard suitable for a general fuzzy model formulation. It also presents a graphical user interface that is a part of a tool for analysis and control of production dynamics, which was developed at the Competence Center for Advanced Control Technologies. The interface is used for eFuMo model identification, validation and simple conversion from an eFuMo model to a standardized XML message and vice versa. The conversion can be carried out via a DOM object, which enables manipulation of XML structure in Matlab, or directly. The XML files that comply with the PMML standard provide a way for applications to describe and exchange models produced by data-mining and machine-learning algorithms. The goal of the presented work is to provide a platform for including fuzzy models into the PMML framework and thus stimulate interoperability of various applications that take advantage of fuzzy models, e.g. in production dynamics analysis and control, as well as in other implementations.

Modeling of space–time infectious disease spread under conditions of uncertainty

A theoretical model for the spread of infectious diseases in a composite space–time domain is developed. The model has a general form that enables it to account for the basic mechanisms of disease distribution and to incorporate the considerable multisourced uncertainty (caused by physiographic features, disease variability, meteorological conditions, etc.). Starting from the general model formulation regarding the specification of transmission and recovery rates, as well as the population migration dynamics, several subsequent assumptions are introduced that simplify analytical tractability and practical implementation. In particular, linearization involving a deterministic functional representation for the average evolution of the fraction of susceptible individuals allows the formulation of an extended Kalman filter approach for estimation based on the time series observed at a finite set of locations. Different aspects of interest derived from the epidemic space–time model proposed, as well as the performance of the extended Kalman filter procedure, are illustrated through simulations.

A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations

Accurate wind power forecasts highly contribute to the integration of wind power into power systems. The focus of the present study is on large-scale offshore wind farms and the complexity of generating accurate probabilistic forecasts of wind power fluctuations at time-scales of a few minutes. Such complexity is addressed from three perspectives: (i) the modeling of a nonlinear and non-stationary stochastic process; (ii) the practical implementation of the model we proposed; (iii) the gap between working on synthetic data and real world observations. At time-scales of a few minutes, offshore fluctuations are characterized by highly volatile dynamics which are difficult to capture and predict. Due to the lack of adequate on-site meteorological observations to relate these dynamics to meteorological phenomena, we propose a general model formulation based on a statistical approach and historical wind power measurements only. We introduce an advanced Markov Chain Monte Carlo (MCMC) estimation method to account for the different features observed in an empirical time series of wind power: autocorrelation, heteroscedasticity and regime-switching. The model we propose is an extension of Markov-Switching Autoregressive (MSAR) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors in each regime to cope with the heteroscedasticity. Then, we analyze the predictive power of our model on a one-step ahead exercise of time series sampled over 10 min intervals. Its performances are compared to state-of-the-art models and highlight the interest of including a GARCH specification for density forecasts.

Thermodynamically constrained averaging theory approach for modeling flow and transport phenomena in porous medium systems: 9. Transition region models

This work is the ninth in a series of papers on the thermodynamically constrained averaging theory (TCAT) approach for modeling flow and transport phenomena in multiscale porous medium systems. A fundamental approach is developed to model the transition region between a two-fluid-phase porous medium system and a single-fluid-phase system, including species transport. A general model formulation is developed along with an entropy inequality to guide the specification of closure relations. The general model formulation and entropy inequality are then used to specify a closed system. The transition region model developed in this work is a generalization and extension of coupling conditions commonly used in sharp interface models. The theoretical framework has multiple areas of potential applicability including terrestrial-atmospheric contact zones, surface water-sediment interface zones, and industrial drying processes.

Development and testing of a distributed urban storm runoff event model with a vector-based catchment delineation

Schematic of the rainfall–runoff process of the TSR model and urban landscape GIS delineation. ► A vector-based urban storm runoff event model is developed and tested. ► The model was set up and evaluated for a small urban catchment in Tokyo. ► Simulated water levels during two events closely reproduced the observed ones. ► The model can be used to evaluate flow and inundation conditions. The recent advances in GIS technology as well as data availability open up new possibilities concerning urban storm runoff modeling. In this paper, a vector-based distributed storm event runoff model – the Tokyo Storm Runoff (TSR) model – is developed and tested for urban runoff analysis using two historical storm events. The set-up of this model is based on urban landscape GIS delineation that faithfully describes the complicated urban land use features in detail. The flow between single spatial elements is based on established hydraulic and hydrological models with equations that describe all aspects of storm runoff generation in an urban environment. The model was set up and evaluated for the small urban lower Ekota catchment in Tokyo Metropolis, Japan. No calibration or tuning was performed, but the general model formulation was used with standard parameter values obtained from the literature. The runoff response to two storm events were simulated; one minor event resulting only in a small-scale flood wave and one major event which inundated parts of the catchment. For both events, the simulated water levels closely reproduced the observed ones. For the major event, also the reported inundation area was well described by the model. It was also demonstrated how the model can be used to evaluate the flow conditions in specific components of the urban hydrological system, which facilitates e.g. evaluation of flood-preventive measures.

Time-Dependent Model of RF Energy Propagation in Coupled Reverberant Cavities

A simple time-dependent model for the analysis of RF energy propagation in coupled reverberant cavities is presented. The cumulative buildup and full exchange of radiated electromagnetic energy between coupled spaces are important issues for electronic systems compatibility in reverberant environments such as below-deck compartments in ships and aircraft cabins and bays. Based on conservation of average energy, the general model formulation is valid for both weak and strong couplings between the cavities caused by window materials, open apertures, closed and open hatches, seams, and cable/pipe penetrations through bulkheads. Application of the model is demonstrated using time-resolved received power measurements conducted in nested reverberation chambers.

An algorithmic framework for solving large-scale multistage stochastic mixed 0–1 problems with nonsymmetric scenario trees

In this paper we present a parallelizable Branch-and-Fix Coordination algorithm for solving medium and large-scale multistage mixed 0–1 optimization problems under uncertainty. The uncertainty is represented via a nonsymmetric scenario tree. An information structuring for scenario cluster partitioning of nonsymmetric scenario trees is also presented, given the general model formulation of a multistage stochastic mixed 0–1 problem. The basic idea consists of explicitly rewriting the nonanticipativity constraints (NAC) of the 0–1 and continuous variables in the stages with common information. As a result an assignment of the constraint matrix blocks into independent scenario cluster submodels is performed by a so-called cluster splitting-compact representation. This partitioning allows to generate a new information structure to express the NAC which link the related clusters, such that the explicit NAC linking the submodels together is performed by a splitting variable representation. The new algorithm has been implemented in a C++ experimental code. Some computational experience is reported on a test of randomly generated instances as well as a large-scale real-life problem by using CPLEX as a solver of the auxiliary submodels within the open source engine COIN-OR.

A model for optimization of process integration investments under uncertainty

The long-term economic outcome of energy-related industrial investment projects is difficult to evaluate because of uncertain energy market conditions. In this article, a general, multistage, stochastic programming model for the optimization of investments in process integration and industrial energy technologies is proposed. The problem is formulated as a mixed-binary linear programming model where uncertainties are modelled using a scenario-based approach. The objective is to maximize the expected net present value of the investments which enables heat savings and decreased energy imports or increased energy exports at an industrial plant. The proposed modelling approach enables a long-term planning of industrial, energy-related investments through the simultaneous optimization of immediate and later decisions. The stochastic programming approach is also suitable for modelling what is possibly complex process integration constraints. The general model formulation presented here is a suitable basis for more specialized case studies dealing with optimization of investments in energy efficiency.

Reverberant Microwave Propagation in Coupled Complex Cavities

A simple model for the analysis of microwave propagation in coupled complex cavities is presented. The cumulative build-up and full exchange of electromagnetic energy between coupled spaces are important issues for wireless systems in reverberant environments, such as below-deck compartments in ships and aircraft cabins and bays. Based on conservation of average energy in the steady state among multiple cavities, the general model formulation is valid for both strong and weak couplings between the cavities caused by window materials, open apertures, closed and open hatches, and cable/pipe penetrations through bulkheads. Application of the model is demonstrated in conjunction with measurements conducted in nested reverberation chambers in the laboratory and in the coupled main weapons bays of a fighter aircraft.

Wireless RF Energy Propagation in Multiply-Connected Reverberant Spaces

Electromagnetic compatibility of electronic systems in reverberant environments is an important issue in the deployment of wireless communications and sensor networks in below-deck compartments of ships and in aircraft cabins and bays. The cumulative buildup and full exchange of radiated electromagnetic energy between multiply-connected reverberant spaces are examined using a simple model adapted from the field of acoustics. Based on conservation of average energy in the diffuse fields, the general model formulation is valid for both weak and strong couplings between the cavities caused by window materials, open apertures, closed and open hatches, seams, and cable/pipe penetrations through bulkheads. Physical energy loss and coupling parameters in three connected chambers are determined from an overlay of model solutions to time-resolved received power measurements, and they can subsequently be used to assess wireless emission coverage and shielding of structures in the connected spaces.

Econometric and stochastic general equilibrium models for evaluation of economic policies

Comparative analysis of economic structure and forecasts generated from simultaneous equation, VAR and autoregressive models based on quarterly series from 1966:1 to 2007:3 of UK to those from the stochastic general equilibrium models has provided insights into objective and subjective evaluation of macro economic impacts of public policies. Econometric estimates are used in formulation of stochastic dynamic general equilibrium models to generate time series of macro variables from stochastic general equilibrium models. Calibrating to ratios, variances, covariance and correlations econometric analyses and general equilibrium models are integrated and shown to be complementary not competitive techniques.

Forecasting with non-homogeneous hidden Markov models

We present a Bayesian forecasting methodology of discrete-time finite state-space hidden Markov models with non-constant transition matrix that depends on a set of exogenous covariates. We describe an MCMC reversible jump algorithm for predictive inference, allowing for model uncertainty regarding the set of covariates that affect the transition matrix. We apply our models to interest rates and we show that our general model formulation improves the predictive ability of standard homogeneous hidden Markov models.

Spiked Dirichlet process prior for Bayesian multiple hypothesis testing in random effects models

We propose a Bayesian method for multiple hypothesis testing in random effects models that uses Dirichlet process (DP) priors for a nonparametric treatment of the random effects distribution. We consider a general model formulation which accommodates a variety of multiple treatment conditions. A key feature of our method is the use of a product of spiked distributions, i.e., mixtures of a point-mass and continuous distributions, as the centering distribution for the DP prior. Adopting these spiked centering priors readily accommodates sharp null hypotheses and allows for the estimation of the posterior probabilities of such hypotheses. Dirichlet process mixture models naturally borrow information across objects through model-based clustering while inference on single hypotheses averages over clustering uncertainty. We demonstrate via a simulation study that our method yields increased sensitivity in multiple hypothesis testing and produces a lower proportion of false discoveries than other competitive methods. While our modeling framework is general, here we present an application in the context of gene expression from microarray experiments. In our application, the modeling framework allows simultaneous inference on the parameters governing differential expression and inference on the clustering of genes. We use experimental data on the transcriptional response to oxidative stress in mouse heart muscle and compare the results from our procedure with existing nonparametric Bayesian methods that provide only a ranking of the genes by their evidence for differential expression.

Optimal prepayment of Dutch mortgages*

Valuation of the prepayment option in Dutch mortgages is complicated. In the Netherlands, mortgagors are not allowed to prepay the full mortgage loan without a compensating penalty. Only a limited amount of the initial mortgage loan can be prepaid penalty‐free. We introduce a general model formulation for the valuation of limited callable mortgages, based on binomial trees. This model can be used for determining both the optimal prepayment strategy and the value of embedded prepayment options. For some mortgage types the prepayment option can be valued exactly, whereas other types require approximative methods for efficient valuation. The heuristic we propose here determines the prepayment option value efficiently and accurately for general mortgage types.

Ecological and Urban Systems Models: Some Explorations of Similarities in the Context of Complexity Theory

There are similarities of form between urban system models and models of ecosystems. These are systematically explored and a general model formulation which embraces both kinds of model is presented. Some insights are gained by using ideas from ecosystem modelling in urban modelling. The biggest gains, however, are for ecosystem modelling. It is demonstrated that urban techniques can be used for incorporating spatial competition effects into such models in novel ways, and that the complex dynamics can then be effectively interpreted. Urban systems have contributed significantly to complexity theory in the past—because they are complicated enough to be interesting but simple enough to be solvable. These insights can now be transferred to complex (spatial) ecosystems. The possibility of joint eco-urban models is explored.

Cost-oriented assembly line balancing: Model formulations, solution difficulty, upper and lower bounds

This paper deals with cost-oriented assembly line balancing. First we focus on the special objective function and a formal problem statement. Then we concentrate on general model formulations that can be solved by standard optimisation tools and introduce several improvements to existent models. These models are designed for either general branch-and-bound techniques with LP-relaxation or general implicit enumeration techniques. Further we discuss the solution difficulty of the problem and show that the “maximally-loaded-station-rule” has to be replaced by the “two-stations-rule”. Compared to the time-oriented version, this causes an enormous increase in solution difficulty. We introduce improved and new bounds for the number of stations and for the relevant costs per product unit. These are used in the general model formulations as well as in specially designed optimisation methods. Finally we give a brief overview of these specially designed methods that are discussed in detail in Amen (2000a,b, 2001).

Multilevel capacitated lot-sizing and resource-constrained project scheduling: an integrating perspective

Some well-known decision problems can be regarded as special cases of the resource-constrained project-scheduling problem (RCPSP) with respect to the structure of their mathematical models. These decision models include assembly line balancing, job shop scheduling and some packing problems. Here, the differences and similarities of mathematical models of the multilevel capacitated lot-sizing problem (MLCLSP) and the RCPSP are shown, and it is concluded that the RCPSP is a special case of the MLCLSP. This theoretical result is complemented by an integrated, general model formulation that allows one to coordinate customer-specific orders and make-to-stock (lot size) production at the master (production) planning level.