On the Necessity of Structure in an Arbitrary World

Abstract

The term response generalization has been poorly defined and has, over many years, been a source of controversy for applied researchers who must grapple with results that show changes in behaviors outside of the response class targeted by their intervention. The present discussion seeks to differentiate response generalization from such terms as response covariation and induction. Instead, response generalization is redefined in the context of response classes and concurrent schedules of reinforcement.     

Identification of the minimum effective dose based on weighted Kaplan–Meier statistics

Testing procedures are considered for identifying the minimum effective dose (MED) in a dose–response study with randomly right-censored survival data, where the MED is defined to be the smallest dose level under study that has survival advantage over the zero dose control. The proposed testing procedures are implemented in a step-down manner together with three different types of weighted Kaplan–Meier statistics. Comparative results of a Monte Carlo error rate and power/bias study for a variety of survival and censoring distributions are then presented and discussed. The application of the proposed procedures is finally illustrated for identifying the MED of the diethylstilbestrol in the treatment of prostate cancer.     

Language Generativity,Response Generalization,and Derived Relational Responding

Language generativity can be described as the ability to produce sentences never before said, and to understand sentences never before heard. One process often cited as underlying language generativity is response generalization. However, though the latter seems to promise a technical understanding of the former at a process level, an investigation of definitions and approaches to the term “response generalization” that appear in the literature suggests that it does not do so. We argue that a more promising candidate for the role of key process underlying language generativity is derived relational responding. We introduce the latter concept and describe empirical research showing its connection with language. We subsequently present a relational frame theory (RFT) conceptualization of derived relations as contextually controlled generalized relational responding. We then review a series of recent studies on derived manding in developmentally delayed children and adults that arguably demonstrate the applied utility of a derived relations-based approach with respect to the phenomenon of generative language.     

Prior Distributions for Item Parameters in IRT Models

The focus of this article is on the choice of suitable prior distributions for item parameters within item response theory (IRT) models. In particular, the use of empirical prior distributions for item parameters is proposed. Firstly, regression trees are implemented in order to build informative empirical prior distributions. Secondly, model estimation is conducted within a fully Bayesian approach through the Gibbs sampler, which makes estimation feasible also with increasingly complex models. The main results show that item parameter recovery is improved with the introduction of empirical prior information about item parameters, also when only a small sample is available.     

Nonparametric estimators for shift in quantal bioassay

Let F(x) and F(x+θ) be log dose-response curves for a standard preparation and a test preparation, respectively, in a parallel quantal bioassay designed to test the relative potency of a drug, toxicant, or some other substance, and suppose the form of F is unknown. Several estimators of the shift parameter θ or relative potency, are compared, including some generalized and trimmed Spearman-Kärber estimators and a non parametric maximum likelihood estimator. Both point and interval estimation are discussed. Some recommendations concerning the choices of estimators are offered.     

Impact of Acquired Immunity and Dose‐Dependent Probability of Illness on Quantitative Microbial Risk Assessment

Dose‐response models in microbial risk assessment consider two steps in the process ultimately leading to illness: from exposure to (asymptomatic) infection, and from infection to (symptomatic) illness. Most data and theoretical approaches are available for the exposure‐infection step; the infection‐illness step has received less attention. Furthermore, current microbial risk assessment models do not account for acquired immunity. These limitations may lead to biased risk estimates. We consider effects of both dose dependency of the conditional probability of illness given infection, and acquired immunity to risk estimates, and demonstrate their effects in a case study on exposure to Campylobacter jejuni. To account for acquired immunity in risk estimates, an inflation factor is proposed. The inflation factor depends on the relative rates of loss of protection over exposure. The conditional probability of illness given infection is based on a previously published model, accounting for the within‐host dynamics of illness. We find that at low (average) doses, the infection‐illness model has the greatest impact on risk estimates, whereas at higher (average) doses and/or increased exposure frequencies, the acquired immunity model has the greatest impact. The proposed models are strongly nonlinear, and reducing exposure is not expected to lead to a proportional decrease in risk and, under certain conditions, may even lead to an increase in risk. The impact of different dose‐response models on risk estimates is particularly pronounced when introducing heterogeneity in the population exposure distribution.     

Adapting Zara’s ‘Pronto Moda’ to a value brand retailer

The clothing sector is currently characterised by frequent assortment rotation in stores, a quick response product development and a focus on minimising end-of-campaign stock levels. This paper is based on a case study carried out at a value brand retailer, with a purpose to show how the company increased competitiveness in a market requiring more responsiveness. The case presented shows that the establishment of an adequate supply strategy for differentiated product segments, the introduction of mini-collections, the redesign of business processes (especially the new product development and the order fulfilment processes) and the redesign of the supply chain, lead to a sales increase and purchasing savings. Moreover, the study treats how a value brand retailer, facing different supply chain challenges compared with the leading brands, balance global and local production, lean and agile manufacturing and how it is possible to abandon the traditional two-campaign paradigm in favour of more rapid replenishment.     

Clergy views on their role in city resilience: lessons from the Canterbury earthquakes

Successful emergency management requires effective communication between the civil defence agencies and the community to provide and receive timely and accurate information, and identify needs and resources. Religious communities encompass a wide cross-section of social classes, socio-economic indicators, ethnic groups and ages, and have their own internal communication structures. In this study, clergy in different Christian denominations were asked about their roles in the Canterbury earthquake sequence (CES), the needs of their congregations and the possibilities and obstacles to deeper collaboration with Civil Defence and Emergency Management (CDEM) authorities. Results show a lack of communication between CDEM and religious communities prior to and during the CES, and record suggestions from the participants on how links between organised religion and CDEM can be strengthened to build resilience for future disasters.     

Implementation of maximin efficient designs in dose‐finding studies

This paper considers the maximin approach for designing clinical studies. A maximin efficient design maximizes the smallest efficiency when compared with a standard design, as the parameters vary in a specified subset of the parameter space. To specify this subset of parameters in a real situation, a four‐step procedure using elicitation based on expert opinions is proposed. Further, we describe why and how we extend the initially chosen subset of parameters to a much larger set in our procedure. By this procedure, the maximin approach becomes feasible for dose‐finding studies. Maximin efficient designs have shown to be numerically difficult to construct. However, a new algorithm, the H‐algorithm, considerably simplifies the construction of these designs. We exemplify the maximin efficient approach by considering a sigmoid Emax model describing a dose–response relationship and compare inferential precision with that obtained when using a uniform design. The design obtained is shown to be at least 15% more efficient than the uniform design. © 2014 The Authors. Pharmaceutical Statistics Published by John Wiley & Sons Ltd.     

Comparison of Ability Estimation Methods Using IRT for Tests with Different Degrees of Difficulty

The aim of the present study is to determine the dependence of the estimation of individual abilities obtained by item response theory (IRT) in relation to the degree of test difficulty and to evaluate how the estimation error may be affected by the estimation method employed. It is shown that abilities in the scale region with little test information are more efficiently estimated using the maximum weighted likelihood estimation (WLE) method, particularly abilities belonging to the upper part of the scale. The study also demonstrates the importance of largest tests for ability estimation.