A three-stage variable selection method for supersaturated designs

A supersaturated design (SSD) is a design whose run size is not enough for estimating all main effects. Such a design is commonly used in screening experiments to screen active effects based on the effect sparsity principle. Traditional approaches, such as the ordinary stepwise regression and the best subset variable selection, may not be appropriate in this situation. In this article, a new variable selection method is proposed based on the idea of staged dimensionality reduction. Simulations and several real data studies indicate that the newly proposed method is more effective than the existing data analysis methods.     

Assumption-aware tools and agency; an interrogation of the primary artifacts of the program evaluation and design profession in working with complex evaluands and complex contexts

Like artisans in a professional guild, we evaluators create tools to suit our ever evolving practice. The tools we use as evaluators are the primary artifacts of our profession, reflect our practice and embody an amalgamation of paradigms and assumptions. With the increasing shifts in evaluation purposes from judging program worth to understanding how programs work, the evaluator’s role is changing to that of facilitating stakeholders in a learning process. This involves clarifying purposes and choices, as well as unearthing critical assumptions. In such a role, evaluators become major tool-users and begin to innovate with small refinements or produce completely new tools to fit a specific challenge or context.We interrogate the form and function of 12 tools used by evaluators when working with complex evaluands and complex contexts. The form is described in terms of traditional qualitative techniques and particular characteristics of the elements, use and presentation of each tool. Then the function of each tool is analyzed with respect to articulating assumptions and affecting the agency of evaluators and stakeholders in complex contexts.     

Optimal designs for additive mixture model with heteroscedastic errors

This paper presents a study of D- and A-optimality of direct sum designs for additive mixture models when the errors are heteroscedastic. Sufficient conditions are given so that D- and A-optimal designs for additive mixture models can be constructed from the D- and A-optimal designs for homogeneous models in sub-mixture systems.     

社会化网络3维用户界面设计分析与研究

为了寻找三维用户界面设计策略，在归纳2维（2D）与2．5维（2．5D）用户界面包含的内容与设计策略的基础上，从3维（3D）用户界面的交互任务、信息呈现和用户搜索行为三方面进行了分析，整合了用户需求、空间行为、空间任务，创新性地构建了面向真3D空间的“需求-任务-层次模型”，为3D用户界面的构建提供了新的思路。此外，从细节层面对3D用户界面具体设计原则进行分析，提出了“信息呈现设计矩阵”，对3D用户界面设计具有一定的指导意义。     

Characterization of singular balanced fractional sm factorial designs derivable from balanced arrays with maximum number of constraints

An upper bound on the maximum number of constraints for s-symbol balanced arrays is derived. It is shown that balanced fractional s^m factorial designs derived from some balanced arrays with the maximum possible number of constraints become singular.     

POWER FUNCTION FOR INVERSE GAUSSIAN REGRESSION MODELS

Inverse Gaussian regression models are useful for data where both the independent and dependent variable are nonnegative and the variance of the dependent variable depends on the independent variable. Zero intercept inverse Gaussian regression models are presented with nonconstant variance, constant ratio of variance to the mean and constant coefficient of variation. The power function for testing hypotheses about the slope is given for all of these models.     

多代合居模式下的卫浴空间通用设计

老年人、青年人、儿童多代合居的家庭,因生活方式的差异致使传统的卫浴空间设计显现出诸多问题。在空间规划、设施配置、氛围营造三个方面引入通用设计理念,合理设计卫浴空间使合居家庭的生活更加融洽。     

The economic design of multivariate binomial EWMA VSSI control charts

Since multi-attribute control charts have received little attention compared with multivariate variable control charts, this research is concerned with developing a new methodology to employ the multivariate exponentially weighted moving average (MEWMA) charts for m-attribute binomial processes; the attributes being the number of nonconforming items. Moreover, since the variable sample size and sampling interval (VSSI) MEWMA charts detect small process mean shifts faster than the traditional MEWMA, an economic design of the VSSI MEWMA chart is proposed to obtain the optimum design parameters of the chart. The sample size, the sampling interval, and the warning/action limit coefficients are obtained using a genetic algorithm such that the expected total cost per hour is minimized. At the end, a sensitivity analysis has been carried out to investigate the effects of the cost and the model parameters on the solution of the economic design of the VSSI MEWMA chart.     

Measuring Sharedness of Team-Related Knowledge: Design and Validation of a Shared Mental Model Instrument

Many researchers have explored how people share and construct similar mental models in teams. They have claimed that successful team performance depends on a shared mental model of team members about task, team, equipment and situation. Most of the literature has illustrated simplified relationships between a team's mental model and their performance without a valid instrument addressing the confined and relevant constructs of a shared mental model. This paper describes the instrument development steps and the conceptual framework for factors associated with shared mental models. After development and refinement, the instrument was finalized for use to measure team-related knowledge. The final instrument consists of 42 items that are linked to the five emergent factors of shared mental models including general task and team knowledge, general task and communication skills, attitude toward teammates and task, team dynamics and interactions, and team resources and working environment.     

Variable Selection via Regression Trees in the Presence of Irrelevant Variables

Many tree algorithms have been developed for regression problems. Although they are regarded as good algorithms, most of them suffer from loss of prediction accuracy when there are many irrelevant variables and the number of predictors exceeds the number of observations. We propose the multistep regression tree with adaptive variable selection to handle this problem. The variable selection step and the fitting step comprise the multistep method.

The multistep generalized unbiased interaction detection and estimation (GUIDE) with adaptive forward selection (fg) algorithm, as a variable selection tool, performs better than some of the well-known variable selection algorithms such as efficacy adaptive regression tube hunting (EARTH), FSR (false selection rate), LSCV (least squares cross-validation), and LASSO (least absolute shrinkage and selection operator) for the regression problem. The results based on simulation study show that fg outperforms other algorithms in terms of selection result and computation time. It generally selects the important variables correctly with relatively few irrelevant variables, which gives good prediction accuracy with less computation time.