Bayes and robust Bayes predictions in a subfamily of scale parameters under a precautionary loss function

ABSTRACT

This paper deals with Bayes, robust Bayes, and minimax predictions in a subfamily of scale parameters under an asymmetric precautionary loss function. In Bayesian statistical inference, the goal is to obtain optimal rules under a specified loss function and an explicit prior distribution over the parameter space. However, in practice, we are not able to specify the prior totally or when a problem must be solved by two statisticians, they may agree on the choice of the prior but not the values of the hyperparameters. A common approach to the prior uncertainty in Bayesian analysis is to choose a class of prior distributions and compute some functional quantity. This is known as Robust Bayesian analysis which provides a way to consider the prior knowledge in terms of a class of priors Γ for global prevention against bad choices of hyperparameters. Under a scale invariant precautionary loss function, we deal with robust Bayes predictions of Y based on X. We carried out a simulation study and a real data analysis to illustrate the practical utility of the prediction procedure.     

Characterization of probability distributions through higher order gap

Weibull, Burr, Pareto and power function distributions have been characterized through the conditional moments of order statistics with higher gap and some of its important deductions are discussed.     

On a class of shrinkage estimators of vector of regression coefficients the

This paper studies a class of shrinkage estimators of the vector of regression coefficients. The small disturbance approximations for the bias and the mean squared error matrix of the estimator are derived. In the sense of mean squared error, these estimators dominate the least squares estimator and the generalized Stein estimator developed by Hosmane (1988).     

The effect of Phase I sample size on the run length performance of control charts for autocorrelated data

Traditional control charts assume independence of observations obtained from the monitored process. However, if the observations are autocorrelated, these charts often do not perform as intended by the design requirements. Recently, several control charts have been proposed to deal with autocorrelated observations. The residual chart, modified Shewhart chart, EWMAST chart, and ARMA chart are such charts widely used for monitoring the occurrence of assignable causes in a process when the process exhibits inherent autocorrelation. Besides autocorrelation, one other issue is the unknown values of true process parameters to be used in the control chart design, which are often estimated from a reference sample of in-control observations. Performances of the above-mentioned control charts for autocorrelated processes are significantly affected by the sample size used in a Phase I study to estimate the control chart parameters. In this study, we investigate the effect of Phase I sample size on the run length performance of these four charts for monitoring the changes in the mean of an autocorrelated process, namely an AR(1) process. A discussion of the practical implications of the results and suggestions on the sample size requirements for effective process monitoring are provided.     

Bayesian Methodology for Model Uncertainty Using Model Performance Data

A simple and useful characterization of many predictive models is in terms of model structure and model parameters. Accordingly, uncertainties in model predictions arise from uncertainties in the values assumed by the model parameters (parameter uncertainty) and the uncertainties and errors associated with the structure of the model (model uncertainty). When assessing uncertainty one is interested in identifying, at some level of confidence, the range of possible and then probable values of the unknown of interest. All sources of uncertainty and variability need to be considered. Although parameter uncertainty assessment has been extensively discussed in the literature, model uncertainty is a relatively new topic of discussion by the scientific community, despite being often the major contributor to the overall uncertainty. This article describes a Bayesian methodology for the assessment of model uncertainties, where models are treated as sources of information on the unknown of interest. The general framework is then specialized for the case where models provide point estimates about a single‐valued unknown, and where information about models are available in form of homogeneous and nonhomogeneous performance data (pairs of experimental observations and model predictions). Several example applications for physical models used in fire risk analysis are also provided.     

Advance Letters as a Means of Improving Respondent Cooperation in Random Digit Dial Studies: A Multistate Experiment

Advance letters can potentially reduce the degree of nonresponsein random digit dial (RDD) surveys; however, they can also havea heterogeneous impact on subgroups, disproportionately raisingparticipation rates among certain segments of the populationand thereby having a detrimental effect on nonresponse bias.This is, in part, because advance letters can only be used inRDD surveys with the subset of respondents for whom an addresscan be identified. It may also be related to who in a householdsees the letter. We assess whether the use of advance letterscan improve the level of participation in the Behavioral RiskFactor Surveillance System (BRFSS) without introducing otherpotential data biases. The data reported here corroborate previousfindings, in terms of the positive impact that advance letterscan have on overall response rates (approximately a 6-percentage-pointgain). Moreover, the advance letters were cost-efficient inthat the cost of obtaining a fixed number of completed surveysusing advance letters was lower than the cost without letters.However, the positive impact of the advance letter on reducingnonresponse may have been offset to some extent in that theadvance letter may have biased the sample of those who completedthe interview toward older, white respondents and those of highersocioeconomic status, and away from younger, nonwhite individualsand persons with lower education and income levels. This lattergroup was underrepresented in the final sample and also lesslike to remember having received a letter.     

A comparative study of U.S. and Iraqi students

This study uses a sample comprised of U.S. students and Iraqi students to determine if differences occur over ethical perceptions based on cultural/demographic issues. Irrespective of demographics, the results of this study indicate significant cultural differences between Iraqi students and American students with regard to selected ethical issues concerning graduate education. Specifically the differences occurred in the students' perceptions of winning is everything, selling one's soul, logic before emotion, and pander to professors. Iraqi students consistently viewed these beliefs as more necessary for success in their graduate education than did their American counterparts.     

Innovation in Strategic Human Resource Management: Building Capacity in the Age of Globalization

This article addresses innovation as a strategic instrument in human resource management capacity building in the age of globalization. To meet the challenges of globalization—negative as well as positive—capacity building is needed in areas of organization, management, governance, and public administration all over the world. Innovation is defined, strategic innovations and innovative strategies are explained, capacity building is delineated, and over 22 areas or realms of innovation are identified, explained, and emphasized as strategic instruments of capacity building. Viewing human resources as human capital and beyond, the article argues that without human resources nothing can be accomplished, and without a well-trained, well-developed, well-appreciated, and well-managed human resources, modern organizations of government and business cannot meet the challenges of the globalization age, which demands a new generation of future-oriented, anticipatory managers who can develop effective visions and manage organizations by riding the high waves of change in the turbulent world.     

Editor's Introduction to the Mini-Symposium on Human Resources Management and Total Quality Management in the Age of Globalization

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