Estimating turning points using polynomial regression

SUMMARY This paper describes a method for estimating regime switches in non-monotonic relationships, using polynomial regressions. Data from the UK financial services industry are used to illustrate the technique. The methodology provides a means of statistically ascertaining the existence of turning points, as well as a means of locating them, should they exist. While the methodology is most suited to applications that involve cross-sectional data, it may also be useful in short-horizon time series turning point prediction.     

Quality Testing and Product Rationing by Input Suppliers

Quality testing by suppliers has significant ramifications for downstream supply chain participants and retail consumers. This article focuses on such implications accounting for the fact that suppliers often enjoy discretion in quality testing and reporting. Under a discretionary testing and reporting environment, we show that a supplier can improve the market's perception of product quality by engaging in self‐imposed production cuts. Production cuts dampen supplier incentives to engage in excessive quality testing, putting the supplier and the market on a more equal information footing. This reduces the market's need to skeptically discount product quality to protect itself. The improved market perception, then, reduces quality testing demand, introducing cost savings. The result that costly production cuts can improve quality perceptions indicates that the groundwork for influencing market perceptions may have to be laid upfront, even prior to acquiring private information, providing a contrast to routine signaling models.     

Sustaining the Benefits of a Quality Initiative through Cooperative Values: A Longitudinal Study

Although research has shown that organizational cultural values influence the success of quality initiatives, how and when cultural values are influential is unclear. This longitudinal study asserts that quality management should be viewed from a dynamic perspective to better understand the role of cooperative cultural values in a quality initiative. We use a multilevel model of quality management, and develop time‐oriented hypotheses using a sociotechnical systems perspective to examine the role of cooperative values. Our analysis uses longitudinal data from over 30 U.S. federal government agencies, collected during an enterprise‐wide quality initiative. We find that, over time, the influence of organization‐level quality practices diminishes, but the influence of cooperative values increases; workgroup‐level quality practices remain consistently important. Our findings reveal the unexplored influences of cooperative values to sustain the benefits of quality management.     

Uncovering the Management Process: An Ethnographic Approach1

The continuing controversy over the extent to which labour management has been transformed in recent years has sustained considerable interest in the basic issue of how management regulates the employment relationship. However, two curious features of these burgeoning discussions are the neglect of small firms and the rarity of intensive fieldwork methods of investigation. This paper examines particular methodological issues arising from an ethnographic study of the small firm-dominated West Midlands clothing sector (Ram, 1994). The ethnographic approach was crucial to the unravelling of the complexities and tensions inherent in the management process. Insights generated by the method allow prevailing views of managerial practices in such settings to be questioned; and more generally, highlight the potential of ethnography as a means of management research.     

Estimation of the parameters in a truncated normal distribution

This paper deals with the estimation of the parameters of doubly truncated and singly truncated normal distributions when truncation points are known. We derive, for these families, a necessary and sufficient condition for the maximum likelihood estimator(MLE) to be finite. Furthermore, the probability of the MLE being infinite is positive. A simulation study for single truncation is carried out to compare the modified maximum likelihood estimator, and the mixed estimator.     

Planned Lead Times in Multistage Systems

This study investigates the impact of planned lead times on performance in multistage manufacturing where material requirements planning is used in a make-to-stock environment. We simulate a variety of different operating environments and find: (1) planned lead times are important to customer service levels under all operating environments examined, but have a smaller impact on inventory investment; (2) tight due dates introduced by short planned lead times hurt customer service without saving much inventory; (3) small increases to tight planned lead times improve customer service substantially with small inventory increases; (4) co-component inventories change with planned lead times, and disparity between such inventories is a sign of poor timing coordination; (5) the fixed order quantity rule performs better than the periodic order quantity rule; and (6) tall product structure and large lot sizes require particular attention to planned lead times. The findings also extend the current understanding of planned lead times by including uncertainties such as forecast error, yield loss, and equipment reliability. The study concludes with a way to diagnose and improve poorly set planned lead times.     

A Bayesian approach to mixture cure models with spatial frailties for population‐based cancer relative survival data

As the treatments of cancer progress, a certain number of cancers are curable if diagnosed early. In population‐based cancer survival studies, cure is said to occur when mortality rate of the cancer patients returns to the same level as that expected for the general cancer‐free population. The estimates of cure fraction are of interest to both cancer patients and health policy makers. Mixture cure models have been widely used because the model is easy to interpret by separating the patients into two distinct groups. Usually parametric models are assumed for the latent distribution for the uncured patients. The estimation of cure fraction from the mixture cure model may be sensitive to misspecification of latent distribution. We propose a Bayesian approach to mixture cure model for population‐based cancer survival data, which can be extended to county‐level cancer survival data. Instead of modeling the latent distribution by a fixed parametric distribution, we use a finite mixture of the union of the lognormal, loglogistic, and Weibull distributions. The parameters are estimated using the Markov chain Monte Carlo method. Simulation study shows that the Bayesian method using a finite mixture latent distribution provides robust inference of parameter estimates. The proposed Bayesian method is applied to relative survival data for colon cancer patients from the Surveillance, Epidemiology, and End Results (SEER) Program to estimate the cure fractions. The Canadian Journal of Statistics 40: 40–54; 2012 © 2012 Statistical Society of Canada     

Semiparametric Bayesian Techniques for Problems in Circular Data

In this paper, we consider the problems of prediction and tests of hypotheses for directional data in a semiparametric Bayesian set-up. Observations are assumed to be independently drawn from the von Mises distribution and uncertainty in the location parameter is modelled by a Dirichlet process. For the prediction problem, we present a method to obtain the predictive density of a future observation, and, for the testing problem, we present a method of computing the Bayes factor by obtaining the posterior probabilities of the hypotheses under consideration. The semiparametric model is seen to be flexible and robust against prior misspecifications. While analytical expressions are intractable, the methods are easily implemented using the Gibbs sampler. We illustrate the methods with data from two real-life examples.