Sequential estimation in multidimensional scaling

The concept of sequential estimation is introduced in multidimensional scaling (MDS). The sequential estimation method developed in this paper refers to continually updating estimates of a configuration as new observations are added. This method has a number of advantages, such as a locally optimal design of the experiment can be easily constructed, and dynamic experimentation is made possible. Using artificial data, the performance of our sequential method is illustrated.We are indebted to anonymous reviewers for their suggestions. In addition, we thank Dr. Frank Critchley for his helpful comments on our Q/S algorithm.     

Maximizing the coefficient of generalizability under the constraint of limited resources

A procedure for maximizing the coefficient of generalizability under the constraint of limited resources is presented. The procedure uses optimization techniques that offer an investigator or test constructor the possibility of employing practical constraints. The procedure is illustrated for the two-facet random-model crossed design.     

A simple technique for improving the quality of parameter estimates in learning hierarchy validation studies

Learning hierarchy research has been characterized by the use of ad hoc statistical procedures to determine the validity of postulated hierarchical connections. The two most substantial attempts to legitimize the procedure are due to White & Clark and Dayton & Macready, although both of these methods suffer from serious inadequacies. Data from a number of sources is analyzed using a restricted maximum likelihood estimation procedure and the results are compared with those obtained using the method suggested by Dayton and Macready. Improved estimates are evidenced by an increase in the computed value of the log likelihood function.     

Answer Sets and Qualitative Optimization

One of the major reasons for the success of answer set programmingin recent years was the shift from a theorem proving to a constraintprogramming view: problems are represented such that stablemodels, respectively answer sets, rather than theorems correspondto solutions. This shift in perspective proved extremely fruitfulin many areas. We believe that going one step further from a"hard" to a "soft" constraint programming paradigm, or, in otherwords, to a paradigm of qualitative optimization, will proveequally fruitful. In this paper we try to support this claimby showing that several generic problems in logic based problemsolving can be understood as qualitative optimization problems,and that these problems have simple and elegant formulationsgiven adequate optimization constructs in the knowledge representationlanguage.     

Computer supported mathematics with Ωmega

Classical automated theorem proving of today is based on ingenious search techniques to find a proof for a given theorem in very large search spaces—often in the range of several billion clauses. But in spite of many successful attempts to prove even open mathematical problems automatically, their use in everyday mathematical practice is still limited.The shift from search based methods to more abstract planning techniques however opened up a paradigm for mathematical reasoning on a computer and several systems of that kind now employ a mix of interactive, search based as well as proof planning techniques.The Ωmega system is at the core of several related and well-integrated research projects of the Ωmega research group, whose aim is to develop system support for a working mathematician as well as a software engineer when employing formal methods for quality assurance. In particular, Ωmega supports proof development at a human-oriented abstract level of proof granularity. It is a modular system with a central proof data structure and several supplementary subsystems including automated deduction and computer algebra systems. Ωmega has many characteristics in common with systems like NuPrL, CoQ, Hol, Pvs, and Isabelle. However, it differs from these systems with respect to its focus on proof planning and in that respect it is more similar to the proof planning systems Clam and λClam at Edinburgh.     

A tribute to Howard Rachlin and his two-parameter discounting model: Reliable and flexible model fitting

Delay discounting reflects the rate at which a reward loses its subjective value as a function of delay to that reward. Many models have been proposed to measure delay discounting, and many comparisons have been made among these models. We highlight the two-parameter delay discounting model popularized by Howard Rachlin by demonstrating two key practical features of the Rachlin model. The first feature is flexibility; the Rachlin model fits empirical discounting data closely. Second, when compared with other available two-parameter discounting models, the Rachlin model has the advantage that unique best estimates for parameters are easy to obtain across a wide variety of potential discounting patterns. We focus this work on this second feature in the context of maximum likelihood, showing the relative ease with which the Rachlin model can be utilized compared with the extreme care that must be used with other models for discounting data, focusing on two illustrative cases that pass checks for data validity. Both of these features are demonstrated via a reanalysis of discounting data the authors have previously used for model selection purposes.     

The tunneling method for global optimization in multidimensional scaling

This paper focuses on the problem of local minima of the STRESS function. It turns out that unidimensional scaling is particularly prone to local minima, whereas full dimensional scaling with Euclidean distances has a local minimum that is global. For intermediate dimensionality with Euclidean distances it depends on the dissimilarities how severe the local minimum problem is. For city-block distances in any dimensionality many different local minima are found. A simulation experiment is presented that indicates under what conditions local minima can be expected. We introduce the tunneling method for global minimization, and adjust it for multidimensional scaling with general Minkowski distances. The tunneling method alternates a local search step, in which a local minimum is sought, with a tunneling step in which a different configuration is sought with the same STRESS as the previous local minimum. In this manner successively better local minima are obtained, and experimentation so far shows that the last one is often a global minimum.This paper is based on the 1994 Psychometric Society's outstanding thesis award of the first author. The authros would like to thank Robert Tijssen of the CWTS Leiden for kindly making available the co-citation data of the Psychometric literature. This paper is an extended version of the paper presented at the Annual Meeting of the Psychometric Society at Champaign-Urbana, Illin., June 1994.     

A simulated annealing methodology for clusterwise linear regression

In many regression applications, users are often faced with difficulties due to nonlinear relationships, heterogeneous subjects, or time series which are best represented by splines. In such applications, two or more regression functions are often necessary to best summarize the underlying structure of the data. Unfortunately, in most cases, it is not known a priori which subset of observations should be approximated with which specific regression function. This paper presents a methodology which simultaneously clusters observations into a preset number of groups and estimates the corresponding regression functions' coefficients, all to optimize a common objective function. We describe the problem and discuss related procedures. A new simulated annealing-based methodology is described as well as program options to accommodate overlapping or nonoverlapping clustering, replications per subject, univariate or multivariate dependent variables, and constraints imposed on cluster membership. Extensive Monte Carlo analyses are reported which investigate the overall performance of the methodology. A consumer psychology application is provided concerning a conjoint analysis investigation of consumer satisfaction determinants. Finally, other applications and extensions of the methodology are discussed.     

关于肿瘤专科诊疗最优化与过度医疗

肿瘤专科诊疗最优化准则有狭义、广义之分,二者既有区别,更有联系.肿瘤专科诊疗的过度医疗的内容是动态发展的,在特定情况下有其积极意义.实现肿瘤专科诊疗最优化,避免过度医疗,要重视矛盾问题"底线"的研究,提供相对宽松的技术、学术、文化氛围;医者持续的、有计划的专业学习与培训是增强其主体性的保证和前提;要充分考虑"可承受性"原则;用新的医德价值观指导现实中的医学道德实践;积极发挥医院伦理委员在肿瘤专科诊疗决策中的作用;建立与利用医务人员诚信档案.     

Developing a Prevention Synthesis and Translation System to Promote Science‐Based Approaches to Teen Pregnancy,HIV and STI Prevention

The Interactive Systems Framework for Dissemination and Implementation (ISF) is a multi‐system framework that can guide research‐to‐practice efforts by building and supporting the work of three interacting systems: the Prevention Delivery, Support, and Synthesis and Translation Systems. The Synthesis and Translation system is vital to bridging science and practice, yet how to develop it and train support system partners to use it is under‐researched. This article bridges this gap by offering a case example of the planning, development, and use of a synthesis and translation product called Promoting Science‐based Approaches to Teen Pregnancy Prevention using Getting To Outcomes. The case presented documents the process used for developing the synthesis and translation product, reports on efforts to engage the Prevention Support system to use the product, and how we approached building interaction between the Synthesis and Translation System and the Support System partners. Practice‐oriented evaluation data are also presented. Implications for practice, policy and research are discussed. Special Issue: Advances in Bridging Research and Practice Using the Interactive System Framework for Dissemination and Implementation; Guest Editors: Abraham Wandersman, Paul Flaspohler, Catherine A. Lesesne, Richard Puddy; Action Editor: Emilie Phillips Smith