Visualizing parameters from loglinear models

Summary  This paper presents a graphical display for the parameters resulting from loglinear models. Loglinear models provide a method for analyzing associations between two or several categorical variables and have become widely accepted as a tool for researchers during the last two decades. An important part of the output of any computer program focused on loglinear models is that devoted to estimation of parameters in the model. Traditionally, this output has been presented using tables that indicate the values of the coefficients, the associated standard errors and other related information. Evaluation of these tables can be rather tedious because of the number of values shown as well as their rather complicated structure, mainly when the analyst needs to consider several models before reaching a model with a good fit. Therefore, a graphical display summarizing tables of parameters could be of great help in this situation. In this paper we put forward an interactive dynamic graphical display that could be used in such fashion.     

A stochastic approach to global error estimation in ODE multistep numerical integration

In order to assess the quality of approximate solutions obtained in the numerical integration of ordinary differential equations related to initial-value problems, there are available procedures which lead to deterministic estimates of global errors. The aim of this paper is to propose a stochastic approach to estimate the global errors, especially in the situations of integration which are often met in flight mechanics and control problems. Treating the global errors in terms of their orders of magnitude, the proposed procedure models the errors through the distribution of zero-mean random variables belonging to stochastic sequences, which take into account the influence of both local truncation and round-off errors. The dispersions of these random variables, in terms of their variances, are assumed to give an estimation of the errors. The error estimation procedure is developed for Adams-Bashforth-Moulton type of multistep methods. The computational effort in integrating the variational equations to propagate the error covariance matrix associated with error magnitudes and correlations is minimized by employing a low-order (first or second) Euler method. The diagonal variances of the covariance matrix, derived using the stochastic approach developed in this paper, are found to furnish reasonably precise measures of the orders of magnitude of accumulated global errors in short-term as well as long-term orbit propagations.     

用可视化方法优化微生物培养基的营养条件

应用可视化方法,对生物发酵培养基的营养条件进行优化.以培养基组成构建多维向量空间,通过降维分析法将多维空间的拓扑结构展现在二维平面上,变成可视化图像,并自动产生目标函数等值线.从中得出同时满足各项指标值的最佳操作区域,并运用逆映射公式将得出最优化区域逆推到原高维空间,进而得到最优培养基组成.以锌酵母为例,介绍可视化方法优化全过程.     

Robust designs for models with possible bias and correlated errors

This paper studies the model-robust design problem for general models with an unknown bias or contamination and the correlated errors. The true response function is assumed to be from a reproducing kernel Hilbert space and the errors are fitted by the qth order moving average process MA（q）, especially the MA（1） errors and the MA（2） errors. In both situations, design criteria are derived in terms of the average expected quadratic loss for the least squares estimation by using a minimax method. A case is studied and the orthogonality of the criteria is proved for this special response. The robustness of the design criteria is discussed through several numerical examples.     

A robust data envelopment analysis model with different scenarios

Input and output data, under uncertainty, must be taken into account as an essential part of data envelopment analysis (DEA) models in practice. Many researchers have dealt with this kind of problem using fuzzy approaches, DEA models with interval data or probabilistic models. This paper presents an approach to scenario-based robust optimization for conventional DEA models. To consider the uncertainty in DEA models, different scenarios are formulated with a specified probability for input and output data instead of using point estimates. The robust DEA model proposed is aimed at ranking decision-making units (DMUs) based on their sensitivity analysis within the given set of scenarios, considering both feasibility and optimality factors in the objective function. The model is based on the technique proposed by Mulvey et al. (1995) for solving stochastic optimization problems. The effect of DMUs on the product possibility set is calculated using the Monte Carlo method in order to extract weights for feasibility and optimality factors in the goal programming model. The approach proposed is illustrated and verified by a case study of an engineering company.     

Optimización de forma de modelos paramétricos bidimensionales basada en ponderación geométrica y reglas evolutivas

This paper presents a method for shape optimization of two-dimensional models subjected to simple or multiple load cases. The optimization is performed iteratively using evolutionary rules, based on the stress level. These rules determine regions on the boundary where the material is underused or overused; the objective is that the model evolves to a minimum weight model with a high and homogeneous stress level. The evolution is performed by modifying the boundary of the model slowly. Since the boundary is defined by parametric B-spline curves, generated changes result in a smooth boundary. The main proposal of the method is that boundary modifications are given by a set of displacements that, its magnitude and direction, take into account the geometrical information of the neighborhood, unlike other methods that simply generate displacement perpendicular to the boundary. Finally, the paper presents the design of a frame bike in order to show the good performance of the method.     

Drawing inferences from logit models for panel data

Logit models have been widely used in marketing to predict brand choice and to make inference about the impact of marketing mix variables on these choices. Most researchers have followed the pioneering example of Guadagni and Little, building choice models and drawing inference conditional on the assumption that the logit model is the correct specification for household purchase behaviour. To the extent that logit models fail to adequately describe household purchase behaviour, statistical inferences from them may be flawed. More importantly, marketing decisions based on these models may be incorrect. This research applies White's robust inference method to logit brand choice models. The method does not impose the restrictive assumption that the assumed logit model specification be true. A sandwich estimator of the covariance ‘corrected’ for possible mis‐specification is the basis for inference about logit model parameters. An important feature of this method is that it yields correct standard errors for the marketing mix parameter estimates even if the assumed logit model specification is not correct. Empirical examples include using household panel data sets from three different product categories to estimate logit models of brand choice. The standard errors obtained using traditional methods are compared with those obtained by White's robust method. The findings illustrate that incorrectly assuming the logit model to be true typically yields standard errors which are biased downward by 10–40 per cent. Conditions under which the bias is particularly severe are explored. Under these conditions, the robust approach is recommended. Copyright © 2000 John Wiley & Sons, Ltd.     

Semiparametric Bayesian measurement error modeling

This work presents a Bayesian semiparametric approach for dealing with regression models where the covariate is measured with error. Given that (1) the error normality assumption is very restrictive, and (2) assuming a specific elliptical distribution for errors (Student-t for example), may be somewhat presumptuous; there is need for more flexible methods, in terms of assuming only symmetry of errors (admitting unknown kurtosis). In this sense, the main advantage of this extended Bayesian approach is the possibility of considering generalizations of the elliptical family of models by using Dirichlet process priors in dependent and independent situations. Conditional posterior distributions are implemented, allowing the use of Markov Chain Monte Carlo (MCMC), to generate the posterior distributions. An interesting result shown is that the Dirichlet process prior is not updated in the case of the dependent elliptical model. Furthermore, an analysis of a real data set is reported to illustrate the usefulness of our approach, in dealing with outliers. Finally, semiparametric proposed models and parametric normal model are compared, graphically with the posterior distribution density of the coefficients.     

Error analysis: a tool for selecting travel demand models

In developing travel demand models it is generally assumed that the base-year data used in developing the parameters, as well as the forecasted data to be used as independent variables for the design year, are of acceptable quality. The purpose of this paper is to present the application of error propagation theory in assesing the predictive quality of one type of travel demand forecasting model (multinomial logit models) and to demonstrate how error considerations can be used as a tool for identifying the optimal model. The general conclusions of this study are that: (1) it is indeed possible to quantify errors in dependent variables in logit models as a consequence of errors in independent variables; and (2) error consideration can be used as a tool for identifying the optimal model from a set of candidate models. Further research is recommended to develop better insights into the phenomenon of error propagation so that the consideration of errors can be a factor in decisions on model selection.     

Comparison and correction of the drop breakup models for stochastic dilute spray flow

Spray–gas interaction is common in many industrial applications that use a liquid jet injection system. Numerous liquid drops interact with the surrounding gas as they travel through the air. During such a travel, aerodynamic interaction between a drop and the surrounding gas flattens the drop and ultimately, breaks up the drop. The TAB (Taylor Analogy Breakup) model was proposed by O’Rourke and Amsden (1987) [6] for the KIVA spray code, but the use of this model has been controversial because the original paper that proposed this model has typographical errors. Another well-known drop breakup model, such as the DDB (Drop Deformation Breakup) model of Ibrahim et al. (1993) [8], has been widely used. However, although numerical solutions of the DDB model ostensibly make it appear superior to those of other previous breakup models, they contain errors that need to be amended. This paper aims to clarify the error controversies of both models; the typographical errors and the erroneous numerical solutions. The complete mathematical derivation of the TAB model is presented, and its correct numerical solutions are compared against the experimental data. We found that the TAB model was superior to other breakup models, such as Clark (1988) [7] and DDB.     

Automatic mesh-healing technique for model repair and finite element model generation

It has been accepted by many researchers that modification of a model is often a necessity as a precursor to effective mesh generation. However, editing the geometry directly is often found to be cumbersome, tedious and expensive. In preparing a CAD model for numerical simulation, one of the critical issues involves the rectification of geometrical and topological errors. Though visually insignificant, these errors hinder the creation of a valid finite element model with a good mesh quality. Most current state-of-the-art works have been trying to heal geometric models directly. The novelty of the method proposed in this paper is that the mesh-healing process includes both model repair and mesh generation in one black box. The mesh-healing algorithm essentially simplifies the problems of the imperfect models and allows one to deal with simple polygons rather than complex surface representations. This paper addresses errors such as gaps, overlaps, T-joints and simple holes.     

离散模糊需求报童问题的可能性模型研究

基于可能性分布函数质心特征值,本文建立了确定离散模糊需求报童问题订货量的利润模型,并分析了成本模型和利润模型的关系。研究结果表明:1)基于可能性分布函数质心的模糊可能性成本和利润模型确定的订货量不一致;2)对应不同订货量,模糊可能性成本与利润之和不为固定常数。数值计算表明:该方法不可取。     

A higher order Markov model for analyzing covariate dependence

During the recent past, there has been a renewed interest in Markov chain for its attractive properties for analyzing real life data emerging from time series or longitudinal data in various fields. The models were proposed for fitting first or higher order Markov chains. However, there is a serious lack of realistic methods for linking covariate dependence with transition probabilities in order to analyze the factors associated with such transitions especially for higher order Markov chains. L.R. Muenz and L.V. Rubinstein [Markov models for covariate dependence of binary sequences, Biometrics 41 (1985) 91–101] employed logistic regression models to analyze the transition probabilities for a first order Markov model. The methodology is still far from generalization in terms of formulating a model for higher order Markov chains. In this study, it is aimed to provide a comprehensive covariate-dependent Markov model for higher order. The proposed model generalizes the estimation procedure for Markov models for any order. The proposed models and inference procedures are simple and the covariate dependence of the transition probabilities of any order can be examined without making the underlying model complex. An example from rainfall data is illustrated in this paper that shows the utility of the proposed model for analyzing complex real life problems. The application of the proposed method indicates that the higher order covariate dependent Markov models can be conveniently employed in a very useful manner and the results can provide in-depth insights to both the researchers and policymakers to resolve complex problems of underlying factors attributing to different types of transitions, reverse transitions and repeated transitions. The estimation and test procedures can be employed for any order of Markov model without making the theory and interpretation difficult for the common users.     

Sensitivity analysis of the newsvendor model

Quality of decisions in inventory management models depends on the accuracy of parameter estimates used for decision making. In many situations, error in decision making is unavoidable. In such cases, sensitivity analysis is necessary for better implementation of the model. Though the newsvendor model is one of the most researched inventory models, little is known about its robustness. In this paper, we perform sensitivity analysis of the classical newsvendor model. Conditions for symmetry/skewness of cost deviation (i.e., deviation of expected demand–supply mismatch cost from its minimum) have been identified. These conditions are closely linked with symmetry/skewness of the demand density function. A lower bound of cost deviation is established for symmetric unimodal demand distributions. Based on demonstrations of the lower bound, we found the newsvendor model to be sensitive to sub-optimal ordering decisions, more sensitive than the economic order quantity model. Order quantity deviation (i.e., deviation of order quantity from its optimum) is explored briefly. We found the magnitude of order quantity deviation to be comparable with that of parameter estimation error. Mean demand is identified as the most influential parameter in deciding order quantity deviation.     

Comparative analysis of UTA multicriteria methods

This paper reviews the main variants of the utility additive (UTA) multicriteria method, and systematically compares their predictive performance on two sets of data. It analyses both those cases where the model provides a ranking with errors and those without errors. First, it shows that the reference projects should be chosen carefully in order to elicit as much information as possible from the decision maker: a set of projects satisfying a fractional factorial plan is recommended. Second, it discusses the use of alternative post-optimality methods for solving the problem of multiple solutions and their different predictive performances. Third, it presents the results of simulations based on utility functions involving interdependence between criteria, and shows that UTA handles this problem effectively by an adjustment of its coefficients. Finally, the influence of the model's parameters on the predictive performance is also investigated.     

Sales and operations planning in systems with order configuration uncertainty

This paper addresses the problem of aligning demand and supply in configure-to-order systems. Using stochastic programming methods, this study demonstrates the value of accounting for the uncertainty associated with how orders are configured. We also demonstrate the value of component supply flexibility in the presence of order configuration uncertainty. We present two stochastic models: an explosion problem model and an implosion problem model. These models are positioned sequentially within a popular business process called sales and operations planning. Both models are formulated as two-stage stochastic programs with recourse and are solved using the sample average approximation method. Computational analyses were performed using data obtained from IBM System and Technology Group. The problem sets used in our analysis are created from actual industry data and our results show that significant improvements in revenue and serviceability can be achieved by appropriately accounting for the uncertainty associated with order configurations.     

Probability models for access security system architectures

This paper presents probability models of access control security system architectures. Access control is the process of screening objects; for example people, baggage, entering a secured area in order to detect and prevent entry by threats such as unauthorized personnel, firearms, explosives. A security system architecture consists of device technologies, as well as operational policies and procedures for utilizing the technologies. The probability models are developed based on Type I (a false alarm is given) and Type II (a threat is not detected) errors. The concept of controlled sampling, in which objects may take different paths through the system, is introduced. New architectures consisting of multiple devices and controlled sampling are proposed and analyzed. The results presented indicate that for specific threat levels, multiple-device systems can be identified which outperform single-device systems for certain error probability measures.     

基于分支定界组合权的综合评价方法

提出一种将主观权重区间和客观权重相结合的线性加权组合权确定方法.通过数学规划模型,不断寻找与组合权重综合评价结果序关系最为接近的主观权重,并由此采用分支定界思想优化主观权重区间,得到具有序关系一致性的最优主观权重.算例分析表明,优化后的主观权重、客观权重和组合权的综合评价结果将带来被评价对象序关系的一致性,验证了该综合评价方法的科学性.     

Fast Identification of Parameters in Optical Systems

This paper presents a scheme for fast identification of parameters in systems that yield large-order measurement vectors. The method can be applied to real-time identification and control. In the paper, the scheme is applied to identification and control of an optical model of a space antenna. Both the mathematical models and numerical results are presented.     

ARMA model order and parameter estimation using genetic algorithms

A new method for simultaneously determining the order and the parameters of autoregressive moving average (ARMA) models is presented in this article. Given an ARMA (p, q) model in the absence of any information for the order, the correct order of the model (p, q) as well as the correct parameters will be simultaneously determined using genetic algorithms (GAs). These algorithms simply search the order and the parameter spaces to detect their correct values using the GA operators. The proposed method works on the principle of maximizing the GA fitness value relying on the deviation between the actual plant output, with or without an additive noise, and the estimated plant output. Simulation results show in detail the efficiency of the proposed approach. In addition to that, a practical model identification and parameter estimation is conducted in this article with results obtained as desired. The new method is compared with other well-known methods for ARMA model order and parameter estimation.