On the α-risks for shewhart control charts

The performance of the usual Shewhart control charts for monitoring process means and variation can be greatly affected by nonnormal data or subgroups that are correlated. Define the α_k-risk for a Shewhart chart to be the probability that at least one “out-of-control” subgroup occurs in k subgroups when the control limits are calculated from the k subgroups. Simulation results show that the α_k-risks can be quite large even for a process with normally distributed, independent subgroups. When the data are nonnormal, it is shown that the α_k-risk increases dramatically. A method is also developed for simulating an “in-control” process with correlated subgroups from an autoregressive model. Simulations with this model indicate marked changes in the α_k-risks for the Shewhart charts utilizing this type of correlated process data. Therefore, in practice a process should be investigated thoroughly regarding whether or not it is generating normal, independent data before out-of-control points on the control charts are interpreted to be due to some real assignable cause.     

Density estimation using asymmetric kernels and Bayes bandwidths with censored data

We propose a modification to the regular kernel density estimation method that use asymmetric kernels to circumvent the spill over problem for densities with positive support. First a pivoting method is introduced for placement of the data relative to the kernel function. This yields a strongly consistent density estimator that integrates to one for each fixed bandwidth in contrast to most density estimators based on asymmetric kernels proposed in the literature. Then a data-driven Bayesian local bandwidth selection method is presented and lognormal, gamma, Weibull and inverse Gaussian kernels are discussed as useful special cases. Simulation results and a real-data example illustrate the advantages of the new methodology.     

NONPARAMETRIC QUANTILE ESTIMATION FROM RECORD-BREAKING DATA

Sometimes, in industrial quality control experiments and destructive stress testing, only values smaller than all previous ones are observed. Here we consider nonparametric quantile estimation, both the ‘sample quantile function’ and kernel-type estimators, from such record-breaking data. For a single record-breaking sample, consistent estimation is not possible except in the extreme tails of the distribution. Hence replication is required, and for m. such independent record-breaking samples the quantile estimators are shown to be strongly consistent and asymptotically normal as m-→∞. Also, for small m, the mean-squared errors, biases and smoothing parameters (for the smoothed estimators) are investigated through computer simulations.     

Some Bayesian lower bounds on reliability in the lognormal distribution

The two-parameter lognormal distribution with density function f(y: γ, σ²) = [(2πσ²)^1/2y] ¹exp[?(ln y ? γ)²/2σ²], y > 0, is important as a failure-time model in life testing. In this paper, Bayesian lower bounds for the reliability function R(t: γ, σ²) = ?[(γ ? ln t)/σ] are obtained for two cases. First, it is assumed that γ is known and σ² has either an inverted gamma or “general uniform” prior distribution. Then, for the case that both γ and σ² are unknown, the normal-gamma prior and Jeffreys' vague prior are considered. Some Monte Carlo simulations are given to indicate some of the properties of the Bayesian lower bounds.     

Designing a Faculty Development Program on Technology

This article illustrates the challenges of creating a faculty development program on technology through the presentation of experiences with one such program, the Virtual Guild. Using examples from the Virtual Guild, the authors focus on the areas of program design and faculty motivation. The discussion of program design involves selecting content and determining the program format. Motivational issues discussed include individual- and organizational-level factors, as well as the combined effect of these factors on faculty participation.     

Understanding How Case Managers Use ‘Sabotage’ as a Frame for Clinically Difficult Situations

Case managers are among the direct service providers responsible for engaging people with severe mental illnesses. Understanding how they interpret and respond to clinically difficult situations can inform ways to intervene and reduce service disengagement. This qualitative study explored clinically difficult situations when case managers invoked the term ??sabotage??. Interviews were conducted with 21 case managers and analyzed by co-coders focusing on how case managers used the term sabotage to describe service user behavior. Themes that emerged were attributing sabotage to; fear of success, fear of attaining what you desire, avoidance of responsibility and change, and a lack of structured support. Drawing on the concepts of clinical case management and mental health recovery, we consider implications for the training and supervision of case managers and how services are structured.     

THE EMERGING ROLE OF THE MEDICAL SOCIOLOGIST IN PALLIATIVE CARE SETTINGS

The demographics of a rapidly aging society and the chronic nature of contemporary illness intersect in the twenty-first century to confront medicine with the challenge of reenvisioning health care at life's end. Using analytic, interpretive, and reflexive tools, the medical sociologist can assist health care teams in improving quality of life terminally ill patients and their caregivers. In this article, we explore the role of the medical sociologist in palliative care settings, discuss ways to integrate that role into the day-to-day processes of palliative medicine, and identify three areas of intervention where medical sociologists can bring a unique perspective to end-of-life settings. We conclude by examining impediments to the inclusion of the medical sociologist on the clinical team and discussing how these barriers can be addressed.     

Families of smooth confidence bands for the survival function under the general random censorship model

Randomly right censored data often arise in industrial life testing and clinical trials. Several authors have proposed asymptotic confidence bands for the survival function when data are randomly censored on the right. All of these bands are based on the empirical estimator of the survival function. In this paper, families of asymptotic (1-)100% level confidence bands are developed from the smoothed estimate of the survival function under the general random censorship model. The new bands are compared to empirical bands, and it is shown that for small sample sizes, the smooth bands have a higher coverage probability than the empirical counterparts.