The probabilistic tolerance design for a subsystem using taguchi's quadratic loss function

Taguchi (1984,1987) has derived tolerances for subsystems, subcomponents, parts and materials. However, he assumed that the relationship between a higher rank and a lower rank quality characteristic is deterministic. The basic structure of the above tolerance design problem is very similar to that of the screening problem. Tang (1987) proposed three cost models and derived an economic design for the screening problem of “the-bigger-the-better” quality characteristic in which the optimal specification limit ( or tolerance ) for a screening variable ( or a lower rank quality characteristic ) was obtained by minimizing the expected total cost function.Tang considered that the quality cost incurred only when the quality characteristic is out of specification while Taguchi considered that the quality cost incurred whenever the quality characteristic deviates from its nominal value. In this paper, a probabilistic relationship, namely, a bivariate normal distribution between the above two qualy characteristics as in a screening problem as well as Taguchi's quadratic loss function are considered together to develop a closed form solution of the tolerance design for a subsystem.     

Linda Milbourne,Voluntary Sector in Transition: Hard Times or New Opportunities?

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations -     

A note on the unification of the Akaike information criterion

To measure the distance between a robust function evaluated under the true regression model and under a fitted model, we propose generalized Kullback–Leibler information. Using this generalization we have developed three robust model selection criteria, AICR*, AICCR* and AICCR, that allow the selection of candidate models that not only fit the majority of the data but also take into account non-normally distributed errors. The AICR* and AICCR criteria can unify most existing Akaike information criteria; three examples of such unification are given. Simulation studies are presented to illustrate the relative performance of each criterion.     

Non-parametric regression approach for model checking on the two-phase regression problem

A non-parametric approach is derived for assessing the goodness of fit for the two-phase regression problem. It is shown that the validity of the constant error variance assumption and the constraint of the continuity of change in two-phase regression can be examined. This method is also used to test nested two-phase regression models. Three numerical examples are presented.     

Assessing perceived value: moderating effects of susceptibility to brand prestige and susceptibility to normative influence

Concerns about individuals’ susceptibility to brand prestige and their social implications of consumption often affect consumer behavior. To clarify such concerns in depth, this study assesses the relationship between brand credibility and perceived value by simultaneously examining the main effects and moderating effects of both susceptibility to brand prestige and susceptibility to normative influence. Empirical testing using a survey of 336 smartphone users from the high-tech and banking industries confirms most of the hypothesized effects in this study. Finally, based on its findings, this study discusses managerial implications as well as research limitations.     

Teaching Note—Creating an Integrative Research Learning Environment for BSW and MSW Students

ABSTRACT

Research courses are often the least popular among BSW and MSW students because the connection between social work practice and research is not always evident. This teaching note introduces the structure of the Social Work integrative Research Lab (SWiRL), which was implemented in a social work program without a doctoral program at a large public university. SWiRL offers students opportunities to engage meaningfully in a variety of social work research projects using a framework of nested mentoring. Students gain hands-on research experience while they develop confidence, leadership, and mentoring skills within this structure. The authors discuss recommendations for other social work programs that do not have doctoral education or an established research environment.     

Dynamic pricing and revenue management process in Internet retailing under uncertainty: An integrated real options approach

Competition and demand volatility often cause modern enterprises to be confronted by uncertain environments. When a firm manages revenue in such competitive and risky environments, the optimization of pricing and capacity allocation, subject to a fixed time and capacity, becomes a complicated problem. Many previous papers concerning revenue management (RM) and pricing require that the firm possesses the ability to know the demand curve (or demand distribution) and set prices on it to maximize profits. However, this assumption may not be the case in some industries. Therefore, this paper focuses on the dynamic lead indicators rather than assumptive lag indicators to establish a concise and flexible decision model for practical use. This paper provides an integrated real options (IRO) approach with analytic hierarchy process (AHP) for the auction RM problem under competitive/dynamic pricing and revenue uncertainty in Internet retailing. A numerical example is also presented to illustrate that the IRO approach can generate better decisions than the naı¨ve (or risk unawareness) approach in revenue quality of safety and profitability. The new perspective and approach proposed by this paper can be extended to other RM fields whenever both profitability and risk are critical to decision making.     

Inference of δ = P(X < Y) for Burr XII distributions with record samples

Let X and Y follow independent Burr type XII distributions, which share a common inner shape parameter. The maximum likelihood estimator of the parameter δ = P(X < Y) is studied based on record samples. The existence and uniqueness of the maximum likelihood estimator of δ based on record samples are established. When the inner shape parameter is known, an exact confidence interval of δ is derived; otherwise, the Fisher information matrix and two bootstrap methods are used to obtain three approximate confidence intervals of δ. The performances of the proposed methods are evaluated via Monte Carlo simulation. Two examples are provided for illustration.     

Regression model selection—a residual likelihood approach

Summary. We obtain the residual information criterion RIC, a selection criterion based on the residual log-likelihood, for regression models including classical regression models, Box–Cox transformation models, weighted regression models and regression models with autoregressive moving average errors. We show that RIC is a consistent criterion, and that simulation studies for each of the four models indicate that RIC provides better model order choices than the Akaike information criterion, corrected Akaike information criterion, final prediction error, C _p and R _adj², except when the sample size is small and the signal-to-noise ratio is weak. In this case, none of the criteria performs well. Monte Carlo results also show that RIC is superior to the consistent Bayesian information criterion BIC when the signal-to-noise ratio is not weak, and it is comparable with BIC when the signal-to-noise ratio is weak and the sample size is large.     

Model selection criteria for dual-inflated data

ABSTRACT

Inflated data are prevalent in many situations and a variety of inflated models with extensions have been derived to fit data with excessive counts of some particular responses. The family of information criteria (IC) has been used to compare the fit of models for selection purposes. Yet despite the common use in statistical applications, there are not too many studies evaluating the performance of IC in inflated models. In this study, we studied the performance of IC for data with dual-inflated data. The new zero- and K-inflated Poisson (ZKIP) regression model and conventional inflated models including Poisson regression and zero-inflated Poisson (ZIP) regression were fitted for dual-inflated data and the performance of IC were compared. The effect of sample sizes and the proportions of inflated observations towards selection performance were also examined. The results suggest that the Bayesian information criterion (BIC) and consistent Akaike information criterion (CAIC) are more accurate than the Akaike information criterion (AIC) in terms of model selection when the true model is simple (i.e. Poisson regression (POI)). For more complex models, such as ZIP and ZKIP, the AIC was consistently better than the BIC and CAIC, although it did not reach high levels of accuracy when sample size and the proportion of zero observations were small. The AIC tended to over-fit the data for the POI, whereas the BIC and CAIC tended to under-parameterize the data for ZIP and ZKIP. Therefore, it is desirable to study other model selection criteria for dual-inflated data with small sample size.