Estimation of the parameters of the Gompertz distribution under the first failure-censored sampling plan

This article presents the goodness-of-fit tests for the Laplace distribution based on its maximum entropy characterization result. The critical values of the test statistics estimated by Monte Carlo simulations are tabulated for various window and sample sizes. The test statistics use an entropy estimator depending on the window size; so, the choice of the optimal window size is an important problem. The window sizes for yielding the maximum power of the tests are given for selected sample sizes. Power studies are performed to compare the proposed tests with goodness-of-fit tests based on the empirical distribution function. Simulation results report that entropy-based tests have consistently higher power than EDF tests against almost all alternatives considered.     

Changing risk preferences in supply chain inventory decisions

The risk preferences of managers may cause their inventory ordering decisions to deviate from the optimal policy. Past studies in operations management have produced mixed results. This study examines this proposition using decision data collected from a supply chain experiment. This article finds that changing the risk preferences of managers with respect to demand changes and supplier failures is a significant behavioural factor in explaining deviations in ordering decisions. This result provides an additional behavioural cause in explaining the bullwhip effect in supply chains. It also provides insights on the challenges for reducing the bullwhip effect in supply chains.     

Does superstition help? A study of the role of superstitions and death beliefs on death anxiety amongst Chinese undergraduates in Hong Kong

Past research has shown that traditional Chinese death beliefs, which mostly consisted of superstitious thoughts, are related to death anxiety. However, other studies have shown that superstitions may help people cope with uncertainty and, therefore, reduce uncertainty-induced anxiety. The role of superstitions, whether related to heightened death anxiety or reduced death anxiety, is unclear. This study attempted to address the knowledge gap by examining the relationships among superstitions and Chinese death beliefs on death anxiety in the Chinese context. One hundred twenty-four undergraduates in Hong Kong completed measures of superstition (R-PBS), death anxiety (MFODS), and Chinese death beliefs scale. Superstition was found to be predictor of death anxiety, as expected. With superstitions highly prevalent in Chinese societies, the study has practical implications in end-of-life care, bereavement support, and death education in the Chinese context.     

Decomposing the Effect of Commercialization on Nonprofit Donations

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - Whether commercialization crowds out nonprofit donations has been a concern for nonprofit professionals and scholars. If...     

A note on the distribution of mean square error of forecasting

Simulation has been a very important and widely used method in the study of misspecification or order determination in time series analysis. Mean square error of forecasting (MSEF) has been a major criterion for comparing the performance of different models. In simulation studies, standard deviations of MSEF's are calculated from the computed values of the MSEF's, In this note, the distribution of MSEF from simulation studies is established. Exact variance of the MSEF can be obtained from the prespecified values of the model selected for simulation. This variance should be a more appropriate criterion for evaluating the performance between models.     

SPECTRAL ANALYSIS IN BUSINESS FORECASTING*

Although spectral analysis has previously been discussed in a number of business journals, the discussion has not been detailed enough for non-mathematicians. The objective of this paper is to review in detail the concepts and to go over the computations of spectral analysis as they pertain to forecasting. To gain insight into the model building technique of spectral analysis, a passing comparison with a familiar model–regression–is made. Regression analysis attempts to find a set of independent variables that shed some light on the dependent variable to be forecasted. In other words, if the independent variables have some functional relationship with the dependent variable, a reliable forecast of the dependent variable can then be made. Forecasting using spectral analysis, on the other hand, is based on the assumption that the variation of a time series can be explained by some mixture of sine and cosine waves. Model parameters can then be estimated for these waves and forecasts be made. These parameters have the same property of least squares as in ordinary regression analysis. A transformation of these parameters gives the spectra of the time series. The spectra are related to the explained variation present in regression analysis. An extension of the spectra gives a set of coefficients of an autoregressive forecasting model. This latter model is referred to as the Wiener-Kolmogorov forecasting model.     

Estimating the Probability of Rare Events Occurring Using a Local Model Averaging

In statistical applications, logistic regression is a popular method for analyzing binary data accompanied by explanatory variables. But when one of the two outcomes is rare, the estimation of model parameters has been shown to be severely biased and hence estimating the probability of rare events occurring based on a logistic regression model would be inaccurate. In this article, we focus on estimating the probability of rare events occurring based on logistic regression models. Instead of selecting a best model, we propose a local model averaging procedure based on a data perturbation technique applied to different information criteria to obtain different probability estimates of rare events occurring. Then an approximately unbiased estimator of Kullback‐Leibler loss is used to choose the best one among them. We design complete simulations to show the effectiveness of our approach. For illustration, a necrotizing enterocolitis (NEC) data set is analyzed.     

A simulation study of dispatch rules for reducing flow times in semiconductor wafer fabrication

Semiconductor wafer fabrication involves very complex process routing, and reducing flow times is very important. This study reports a search for better dispatch rules for achieving the goal of reducing flow times, while maintaining high machine utilization. We explored a new simulation-based dispatch rule and a queue prediction dispatch rule. Using simulation experiments and an industrial data set, we also compared several other dispatch rules commonly used in semiconductor manufacturing with our proposed dispatch rules. Among these rules, in addition to the simulation-based dispatching rule, the shortest-remaining-processing-time, earliest-due-date and leastslack rules also performed well in terms of reducing flow times. The reasons behind these good rules are discussed in this paper. Based on the previous works and this study, accurately predicting and effectively utilizing future flow times can improve the quality of production management decisions.