Asymptotic Comparison Between Constant-stress Testing and Step-stress Testing for Type-I Censored Data from Exponential Distribution

By running the life tests at higher stress levels than normal operating conditions, accelerated life testing quickly yields information on the lifetime distribution of a test unit. The lifetime at the design stress is then estimated through extrapolation using a regression model. In constant-stress testing, a unit is tested at a fixed stress level until failure or the termination time point of the test, while step-stress testing allows the experimenter to gradually increase the stress levels at some pre-fixed time points during the test. In this article, the optimal k-level constant-stress and step-stress accelerated life tests are compared for the exponential failure data under Type-I censoring. The objective is to quantify the advantage of using the step-stress testing relative to the constant-stress one. A log-linear relationship between the mean lifetime parameter and stress level is assumed and the cumulative exposure model holds for the effect of changing stress in step-stress testing. The optimal design point is then determined under C-optimality, D-optimality, and A-optimality criteria. The efficiency of step-stress testing compared to constant-stress testing is discussed in terms of the ratio of optimal objective functions based on the information matrix.     

On Variational Bayes Estimation and Variational Information Criteria for Linear Regression Models

Variational Bayes (VB) estimation is a fast alternative to Markov Chain Monte Carlo for performing approximate Baesian inference. This procedure can be an efficient and effective means of analyzing large datasets. However, VB estimation is often criticised, typically on empirical grounds, for being unable to produce valid statistical inferences. In this article we refute this criticism for one of the simplest models where Bayesian inference is not analytically tractable, that is, the Bayesian linear model (for a particular choice of priors). We prove that under mild regularity conditions, VB based estimators enjoy some desirable frequentist properties such as consistency and can be used to obtain asymptotically valid standard errors. In addition to these results we introduce two VB information criteria: the variational Akaike information criterion and the variational Bayesian information criterion. We show that variational Akaike information criterion is asymptotically equivalent to the frequentist Akaike information criterion and that the variational Bayesian information criterion is first order equivalent to the Bayesian information criterion in linear regression. These results motivate the potential use of the variational information criteria for more complex models. We support our theoretical results with numerical examples.     

A location analysis approach for military maintenance scheduling with geographically dispersed service areas

This research uses a two-stage maximal covering location problem (MCLP) model to develop Inter continental ballistic missile (ICBM) maintenance schedules for the US Air Force. Solutions are compared to actual missile maintenance activities accomplished at F. E. Warren Air Force Base (AFB), Wyoming in May 2005. Sensitivity analysis is performed to determine the impact of altering security response times and the number of security patrol areas on the quality of daily maintenance schedules and personnel usage. Results indicate marked improvement over traditional Air Force scheduling methods. In addition sensitivity analysis identifies the levels at which the quality and quantity of maintenance performance is impacted.     

The role of dualities in arbitrating continuity and change in forms of organizing

A substantial body of literature on new forms of organizing has forecast the end of bureaucracy. More recent empirical studies, however, indicate that high‐performing organizations are adopting dual forms of organizing in which the controllability advantages associated with traditional forms work to complement and support the responsiveness attributes of new forms of organizing. The paradox is that, if organizations discard the key planning, co‐ordinating and direction‐setting mechanisms of traditional forms of organizing, they also remove the stabilizing dimensions of organizational form that are essential in periods of uncertainty and change. The challenge for organizations lies in learning how to manage the tensions or dualities between traditional and new forms of organizing, a process demanding the arbitration of continuity and change. This paper explores the concept of dualities and its salience in the management of organizing forms. First, the nature of dualities is explained; secondly, a set of characteristics is developed to describe the behaviour of dualities; and thirdly, suggestions are presented for arbitrating the tensions that exist in organizing form dualities. These three contributions are relevant because they signal the route to the effective creation and management of organizing form dualities, the benefit of which is the constructive combination of dynamic capabilities (underpinning innovation and responsiveness, the hallmarks of new forms of organizing) and operational capabilities (underpinning stability and efficiency, the hallmarks of traditional forms of organizing).     

Improved adaptive group testing algorithms with applications to multiple access channels and dead sensor diagnosis

We study group-testing algorithms for resolving broadcast conflicts on a multiple access channel (MAC) and for identifying the dead sensors in a mobile ad hoc wireless network. In group-testing algorithms, we are asked to identify all the defective items in a set of items when we can test arbitrary subsets of items. In the standard group-testing problem, the result of a test is binary—the tested subset either contains defective items or not. In the more generalized versions we study in this paper, the result of each test is non-binary. For example, it may indicate whether the number of defective items contained in the tested subset is zero, one, or at least two. We give adaptive algorithms that are provably more efficient than previous group testing algorithms. We also show how our algorithms can be applied to solve conflict resolution on a MAC and dead sensor diagnosis. Dead sensor diagnosis poses an interesting challenge compared to MAC resolution, because dead sensors are not locally detectable, nor are they themselves active participants. A preliminary version of this paper was presented at SPAA 2006.     

Rising food security concerns among New Zealand adolescents and association with health and wellbeing

The current paper explores recent changes in food insecurity among adolescents and determine if food security concerns are associated with poorer wellbeing. Data were collected as part of two nationally representative surveys of the health and wellbeing of high-school students in New Zealand in 2007 and 2012. In 2012, 11% of young people reported food security concerns often or always, with 33% reporting food security concerns occasionally/sometimes. The prevalence of food security concerns at both frequencies increased significantly from 2007 (8% and 28%, respectively). Young people with food security concerns were more likely to report poor indicators of health and wellbeing, including truancy, poor general health, mental health concerns and obesity. Our findings highlight the growing concerns of food insecurity reported by adolescents in New Zealand. Interventions that address food security for families may provide a tangible means of promoting the healthy development of children and young people.     

Modern variable selection for longitudinal semi-parametric models with missing data

Penalized methods for variable selection such as the Smoothly Clipped Absolute Deviation penalty have been increasingly applied to aid variable section in regression analysis. Much of the literature has focused on parametric models, while a few recent studies have shifted the focus and developed their applications for the popular semi-parametric, or distribution-free, generalized estimating equations (GEEs) and weighted GEE (WGEE). However, although the WGEE is composed of one main and one missing-data module, available methods only focus on the main module, with no variable selection for the missing-data module. In this paper, we develop a new approach to further extend the existing methods to enable variable selection for both modules. The approach is illustrated by both real and simulated study data.     

More nonparametric Bayesian inference in applications

Discussion of “Nonparametric Bayesian Inference in Applications” by Peter Mueller, Fernando A. Quintana, Garritt Page: More Nonparametric Bayesian Inference in Applications.     

A Leadership Training Group for at Risk Fifth & Seventh Graders: Results from A Brief Strength-Based Group Program

ABSTRACT

An increasing number of youth are exhibiting social, emotional, and behavioral problems that hinder their ability to function at grade level. Subsequently, school mental health services have not been able to address the need for services particularly among students who are minority and poor. A mixed methodology study was conducted to determine the treatment outcomes from a brief strength-based leadership training group for primarily students who are African American and poor. Pre–post scores on three scales and focus group data revealed significant positive changes in regard to internal areas of functioning and social skills, anger management skills, and school attitude. Gender and age significantly impacted outcomes.