Saturated main-effect designs for factorial experiments

The results of a computer search for saturated designs for 2ⁿ factorial experiments with n runs is reported, (where n = 2 mod 4). A complete search of the design space is avoided by focussing on designs constructed from cyclic generators. A method of searching quickly for the best generators is given. The resulting designs are as good as, and sometimes better than, designs obtained via search algorithms reported in the literature. The addition of a further factor having three levels is also considered. Here, too, a complete search is avoided by restricting attention to the most efficient part of the design space under p-efficiency.     

Search for optimal designs in a three stagenested random model

In this paper we define a class of unbalanced designs, denoted by C_k,s,t, for estimating the components of variance in a k-stage nested random effects linear model. This class contains many of the designs proposed in the literature for nested components of variance models. We focus on the three-state model and discuss the determination of locally optimal designs within this class using a systematic computer search. For large sample sizes we show that approximate optimal designs may be obtained using a limit argument combined with numerical optimization. A comparison of our designs with previously published designs suggests that, in many cases, our designs result in substantial gains in efficiency.     

Constructing optimal designs for fitting pharmacokinetic models

We consider some computational issues that arise when searching for optimal designs for pharmacokinetic (PK) studies. Special factors that distinguish these are (i) repeated observations are taken from each subject and the observations are usually described by a nonlinear mixed model (NLMM), (ii) design criteria depend on the model fitting procedure, (iii) in addition to providing efficient parameter estimates, the design must also permit model checking, (iv) in practice there are several design constraints, (v) the design criteria are computationally expensive to evaluate and often numerical integration is needed and finally (vi) local optimisation procedures may fail to converge or get trapped at local optima.We review current optimal design algorithms and explore the possibility of using global optimisation procedures. We use these latter procedures to find some optimal designs.For multi-purpose designs we suggest two surrogate design criteria for model checking and illustrate their use.     

An access network design problem with end-to-end QoS constraints

In this paper, we present an access network design problem with end-to-end quality of service (QoS) requirement. The problem can be conceptualized as a two-level hierarchical location-allocation problem on the tree topology with nonlinear side constraints. The objective function of the nonlinear mixed integer programming model minimizes the total cost of switch and fiber cable, while satisfying demand within the prescribed level of QoS. By exploiting the inherent structure of the nonlinear QoS constraints, we develop linearization techniques for finding an optimal solution. Also, we devise an effective exact optimal algorithm within the context of disjunctive constraint generation. We present promising computational results that demonstrate the effectiveness of the proposed solution procedure.     

Improving Risk Management: From Lame Excuses to Principled Practice

The three classic pillars of risk analysis are risk assessment (how big is the risk and how sure can we be?), risk management (what shall we do about it?), and risk communication (what shall we say about it, to whom, when, and how?). We propose two complements as important parts of these three bases: risk attribution (who or what addressable conditions actually caused an accident or loss?) and learning from experience about risk reduction (what works, and how well?). Failures in complex systems usually evoke blame, often with insufficient attention to root causes of failure, including some aspects of the situation, design decisions, or social norms and culture. Focusing on blame, however, can inhibit effective learning, instead eliciting excuses to deflect attention and perceived culpability. Productive understanding of what went wrong, and how to do better, thus requires moving past recrimination and excuses. This article identifies common blame‐shifting “lame excuses” for poor risk management. These generally contribute little to effective improvements and may leave real risks and preventable causes unaddressed. We propose principles from risk and decision sciences and organizational design to improve results. These start with organizational leadership. More specifically, they include: deliberate testing and learning—especially from near‐misses and accident precursors; careful causal analysis of accidents; risk quantification; candid expression of uncertainties about costs and benefits of risk‐reduction options; optimization of tradeoffs between gathering additional information and immediate action; promotion of safety culture; and mindful allocation of people, responsibilities, and resources to reduce risks. We propose that these principles provide sound foundations for improving successful risk management.     

Designing for “touch”, “reach” and “movement” in postwar (1946–1972) English primary and infant school environments

ABSTRACT

This article examines the use and interpretation of the terms “touch”, “reach” and “movement” in Ministry of Education (later, Department of Education) official publications known as Building Bulletins between the years 1949–1972. A close critical reading of Building Bulletins concerned primarily with school design for young children (infant and primary schools) in the English context has been carried out and the results of this exercise are discussed in the wider context of close relationships established between architects designing schools and leading progressive educationalists in Britain. The wider international context, particularly progressive educational design in the USA, is used to further understand the use and interpretation of these terms. The article contributes to a current interest among historians of education in exploring material and sensory histories of schooling.     

Replication: Revisiting Tversky and Shafir’s (1992) Disjunction Effect with an extension comparing between and within subject designs

Does uncertainty about an outcome influence decisions? The sure-thing principle (Savage, 1954) posits that it should not, but Tversky and Shafir (1992) found that people regularly violate it in hypothetical gambling and vacation decisions, a phenomenon they termed “disjunction effect”. Very close replications and extensions of Tversky and Shafir (1992) were conducted in this paper (N = 890, MTurk). The target article demonstrated the effect using two paradigms in a between-subject design: here, an extension also testing a within-subject design, with design being randomly assigned was added. These results were consistent with the original findings for the “paying to know“ problem (original: Cramer’s V = 0.22, 95% (CI) [0.14, 0.32]; replication: Cramer’s V = 0.30, 95% CI [0.24, 0.37]), yet not for the “choice under risk” problem (original: Cramer’s V = 0.26, 95% CI [0.14, 0.39]; replication: Cramer’s V = 0.11, 95% CI [−0.07, 0.20]). The within-subject extension showed very similar results. Implications for the disjunction effect and judgment and decision-making theory are discussed, and a call for improvements on the statistical understanding of comparisons of between-subject and within-subject designs is introduced. All materials, data, and code are available on https://osf.io/gu58m/.     

Distribution of the two‐sample t‐test statistic following blinded sample size re‐estimation

We consider the blinded sample size re‐estimation based on the simple one‐sample variance estimator at an interim analysis. We characterize the exact distribution of the standard two‐sample t‐test statistic at the final analysis. We describe a simulation algorithm for the evaluation of the probability of rejecting the null hypothesis at given treatment effect. We compare the blinded sample size re‐estimation method with two unblinded methods with respect to the empirical type I error, the empirical power, and the empirical distribution of the standard deviation estimator and final sample size. We characterize the type I error inflation across the range of standardized non‐inferiority margin for non‐inferiority trials, and derive the adjusted significance level to ensure type I error control for given sample size of the internal pilot study. We show that the adjusted significance level increases as the sample size of the internal pilot study increases. Copyright © 2016 John Wiley & Sons, Ltd.     

Symmetrical Design of Experiment in Global Sensitivity Analysis Based on ANOVA High-dimensional Model Representation

This article reviews symmetrical global sensitivity analysis based on the analysis of variance of high-dimensional model representation. To overcome the computational difficulties and explore the use of symmetrical design of experiment (SDOE), two methods are presented. If the form of the objective function f is known, we use SDOE to estimate the symmetrical global sensitivity indices instead of Monte Carlo or quasi-Monte Carlo simulation. Otherwise, we use the observed values of the experiment to do symmetrical global sensitivity analysis. These methods are easy to implement and can reduce the computational cost. An example is given by symmetrical design of experiment.     

Understanding Estimators of Treatment Effects in Regression Discontinuity Designs

In this paper, we propose two new estimators of treatment effects in regression discontinuity designs. These estimators can aid understanding of the existing estimators such as the local polynomial estimator and the partially linear estimator. The first estimator is the partially polynomial estimator which extends the partially linear estimator by further incorporating derivative differences of the conditional mean of the outcome on the two sides of the discontinuity point. This estimator is related to the local polynomial estimator by a relocalization effect. Unlike the partially linear estimator, this estimator can achieve the optimal rate of convergence even under broader regularity conditions. The second estimator is an instrumental variable estimator in the fuzzy design. This estimator will reduce to the local polynomial estimator if higher order endogeneities are neglected. We study the asymptotic properties of these two estimators and conduct simulation studies to confirm the theoretical analysis.