A non parametric approach to monitor simple linear profiles in phases I and II

In this paper, a non parametric approach is first proposed to monitor simple linear profiles with non normal error terms in Phase I and Phase II. In this approach, two control charts based on a transformation technique and decision on beliefs are designed in order to monitor the intercept and the slope, simultaneously. Then, some simulation experiments are performed in order to evaluate the performance of the proposed control charts in Phase II under both step and drift shifts in terms of out-of-control average run length (ARL₁). Besides, the performance of the proposed control charts is compared to the ones of seven other existing schemes in the literature. Simulation results show that the proposed control charts outperform the other control charts in detecting both the small step and small drift shifts of intercept. However, they have a weaker performance compared to other control charts in detecting both small step and small drift shifts of the slope. At the end, a real example from an electronic industry is used to illustrate the implementation of the proposed method.     

An absolutely continuous bivariate Topp–Leone distribution: A useful model on a bounded domain

We introduce an absolutely continuous bivariate generalization of the Topp–Leone distribution, which is a special member of the proportional reversed hazard family using a one-parameter bivariate exchangeable distribution. We show that a copula approach could also be used in defining the bivariate Topp–Leone distribution. The marginal distributions of the new bivariate distribution have also Topp–Leone distributions. We study its distributional and dependence properties. We estimate the parameters by maximum-likelihood procedure, perform a simulation study on the estimators, and apply them to a real data set. Furthermore, we give a way of generating bivariate distributions using the proposed distribution.     

Search With Learning for Differentiated Products: Evidence from E-Commerce

This article provides a method to estimate search costs in a differentiated product environment in which consumers are uncertain about the utility distribution. Consumers learn about the utility distribution by Bayesian updating their Dirichlet process prior beliefs. The model provides expressions for bounds on the search costs that can rationalize observed search and purchasing behavior. Using individual-specific data on web browsing and purchasing behavior for MP3 players sold online we show how to use these bounds to estimate search costs as well as the parameters of the utility distribution. Our estimates indicate that search costs are sizable. We show that ignoring consumer learning while searching can lead to severely biased search cost and elasticity estimates.     

Financial hardship assistance behind the scenes: Insights from financial counsellors

Financial hardship, in a credit society such as Australia, can affect almost anyone. To protect consumers from the negative impacts of financial hardship—which can include the stresses of enforcement action and disconnection from essential services—legal protections have been incorporated into the regulatory frameworks for the consumer credit, energy, water and telecommunications sectors. In this article, we outline the findings of our study, which used a survey of financial counsellors around Australia and focus group interviews with Victorian financial counsellors to examine how these legal protections are being implemented by service providers in these four sectors. Our findings highlight a tendency on the part of service providers to take a generic, one‐size‐fits‐all approach to compliance with these legal protections that prevents them from effectively assisting consumers struggling with debt. We discuss the particular shortcomings of this approach in the context of consumers living on low incomes—especially Centrelink incomes—and outline the policy implications of our findings for assisting these vulnerable groups.     

Project HOPE: Evaluation of Health Science Career Education Programming for Rural Latino and European American Youth

Project HOPE comprises a health science career pipeline program based in social cognitive career theory. The program aims to promote health science career exploration for rural and Latino middle school students while simultaneously emphasizing the importance of achieving in math and science subjects. The authors conducted 2 studies to evaluate Project HOPE using baseline and follow‐up evaluation data. Results of Study 1 indicated that Project HOPE was associated with increases in math/science self‐efficacy for both Latino (n = 42) and European American (n = 31) students. Results of Study 2 indicated that Project HOPE was associated with higher health science career self‐efficacy beliefs for European American students (n = 22) and higher health science career interests for Latino students (n = 28). These results suggest that domain‐specific career interventions for middle school students that focus on jobs tied to the local economy may be a useful way to engage students in career exploration.     

Monitoring process mean using generally weighted moving average chart for exponentially distributed characteristics

In this article, we will present a control chart using normal transformation and generally weighted moving average (GWMA) statistic when the quality characteristic follows the exponential distribution. We will develop the necessary measures to monitor the mean of the process using GWMA statistic and analyze the performance using simulation. The average run lengths for monitoring process average are given for various process shifts. The performance of the proposed chart is examined and compared with the existing control chart. The proposed control chart is effective for the monitoring of small shifts in the mean process. The application of the proposed chart is illustrated with the help of simulated data.     

Using informative priors for handling missing data problem in Cox regression

The aim of this study is to determine the effect of informative priors for variables with missing value and to compare Bayesian Cox regression and Cox regression analysis. For this purpose, firstly simulated data sets with different sample size within different missing rate were generated and each of data sets were analysed by Cox regression and Bayesian Cox regression with informative prior. Secondly lung cancer data set as real data set was used for analysis. Consequently, using informative priors for variables with missing value solved the missing data problem.     

Evidence from the Chernobyl Nuclear Accident: The Effect on Health,Education, and Labor Market Outcomes in Belarus

The Chernobyl nuclear accident of 1986 had deleterious health consequences for the population of Belarus, especially for those who were children at the time of the disaster. Using the 2003–2008 waves of the Belarusian Household Survey of Income and Expenditure (BHSIE), we estimate the effect of radiation exposure on the health, education, and labor market outcomes among cohorts and areas affected by the accident, utilizing the nuclear accident as a natural experiment. We find that young individuals who came from the most contaminated areas had worse health, were less likely to hold university degrees, were less likely to be employed, and had lower wages compared to those who were older at the time of the accident and who came from less contaminated areas.     

Does Demographic Transition with Human Capital Dynamics Matter for Economic Growth? A Dynamic Panel Data Approach to GMM

Social Indicators Research - This study seeks to empirically investigate whether demographic transition with dynamics of human capital matters for economic growth for a representative sample of...