THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

Economic Control of Quality of Manufactured Product.

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

(1991). Applied Linear Regression Models. Journal of Quality Technology: Vol. 23, No. 1, pp. 76-77.

Applied Linear Regression Models

In this study, the integration of total quantity discount policy in materials procurement of a project scheduling problem is considered. The problem is mathematically formulated and a hybrid meta-heuristic method called GA-PSO is developed for the problem. The proposed algorithm is tested on 810 benchmarks and is compared with optimal and near optimal solutions.

Material ordering with discount policy integrated with a project scheduling problem

Stroke is one of the three most common causes of death and the sixth most common cause of disability worldwide. Building effective and efficient stroke care systems is critical for improving patient outcomes in the prevention, treatment, and rehabilitation of stroke. A systems approach is necessary to improve the way stroke is treated so that patients have access to the most appropriate treatment in centers that are best equipped to deal with their critical and time-sensitive needs. System simulation has much to contribute to the design and operation of effective and efficient stroke care systems. The success on this path depends, among other factors, upon common vision for problems to attack. The objective of this paper is to review existing contribution of simulation modeling to stroke care systems and to propose the ways for future contribution of system simulation to the effort of designing and operating effective stroke care systems.

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Stroke care systems: can simulation modeling catch up with the recent advances in stroke treatment?

Due to the significant role of water use in socio-economic development, competition for a larger share in water allocation is intense among stakeholders in watersheds. In this paper, three optimization models are developed to explore the optimal allocation of water among users, thus to resolve water conflicts in watersheds. The first model is a linear programming model. The objective function of this model maximizes the basins' revenue. The second model is a multi-objective optimization model. The objective functions of this model maximize the marginal value of water for stakeholders when environmental water supply is a constraint in the model. The third model is also a multi-objective programming model. Three objective functions; maximizing a basin revenue, minimizing the environmental water shortages, and minimizing water transfer from upstream area to downstream region are considered. These models are applied to the Sefidrud Basin, a watershed with eight administrative provinces, each considered as a stakeholder for the modelling. The formulations of three water allocation models are presented and the results of their implementation are compared. (cont.)

Multi-objective decision making for basin water allocation

The manufacturing operations often involve multistage processes where the process capability of each stage is affected by the process capability of its precedent processes. This property is known as the cascade property. The purpose of this paper is to estimate the process capability of the second stage of two-stage process while the cascade property impact is removed using residuals analysis. To this end, a method is proposed to determine the specification limits of the residuals based on the specification limits of the quality characteristics in the first and second stages. The C
 p, C
 pk and S
 pk indices are used to calculate the capability of the second stage in the two-stage process. The results of simulation study show the satisfactory performance of the proposed method in estimating the pure process capability of the second stage.

/pdf/residuals-based-process-capability-indices-for-two-stage-1elagblx2h.pdf

Residuals based process capability indices for two-stage processes

Bushfire is an important public health and safety issue in Australia and in the world. Late evacuation has been identified as a key issue in emergency evacuation management, which has the potential to increase the risk of injury or death. For example, in the Australian Black Saturday 2009 bushfire, a large number of evacuees could not be evacuated on within the restricted time window of 10 minutes, which has resulted in the death of 119 people - 68 per cent of the total number of fatalities. Late evacuation is the most crucial stage in the emergency response because of the hard constraint of limited time window, uncertainty in the trajectory of bushfire and the resulted road disruptions, and evacuation of relatively vulnerable segments of the community. The paper developed a multi-objective optimization model to improve the decision-making process to evaluate late evacuees from assembly points to shelters. Scenarios were generated to incorporate different constraints such as the time-window, road disruptions, and capacity constraints. The model is solved by Ɛ-constraint approach. Several hypothetical bushfire scenarios in two different case studies are provided to evaluate the model. Objective functions were simultaneously optimized to enable maximizing the total number of people evacuated and optimizing the assignment of available resources as the number of rescue vehicles and shelters. The model also calculates the number of people who could not be evacuated given the time and resources available to do so. The authors argue that this model provides a scenario-based evidence for effective decision-making, which helps minimizing resource utilization and maximizing coverage of late evacuees.

Babak Abbasi

Papers

Material ordering with discount policy integrated with a project scheduling problem

Stroke care systems: can simulation modeling catch up with the recent advances in stroke treatment?

Multi-objective decision making for basin water allocation

Residuals based process capability indices for two-stage processes

Enhancing late evacuation emergency response in bushfire-prone areas: a multi-objective optimization approach